Difference between revisions of "2011 EncyclopediaOfMachineLearning AuthoredTerms"

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|([[Yoav Shoham; Rob Powers, 2011]]) ⇒ [[Yoav Shoham]]; Rob Powers. ([[2011]]). “Multi-Agent Learning I: Problem Definition.” In: ([[Sammut & Webb, 2011]])
 
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Latest revision as of 14:40, 13 August 2019


ID Term Page Type Redirect Author(s) mult alp Synonym Cross References GM-RKB Entry
1 Abduction 3 A Antonis C. Kakas Explanation-Based Learning ; Inductive Logic Programming (Antonis C. Kakas, 2011) ⇒ Antonis C. Kakas. (2011). “Abduction.” In: (Sammut & Webb, 2011)
6 Active Learning 10 A David Cohn Active Learning Theory (David Cohn, 2011) ⇒ David Cohn. (2011). “Active Learning.” In: (Sammut & Webb, 2011)
7 Active Learning Theory 14 A Sango Dasgupta Active Learning (Sango Dasgupta, 2011) ⇒ Sango Dasgupta. (2011). “Active Learning Theory.” In: (Sammut & Webb, 2011)
10 Adaptive Real-Time Dynamic Programming 20 A Andrew G. Barto ARTDP Anytime Algorithm ; Approximate Dynamic Programming ; Reinforcement Learning ; System Identification (Andrew G. Barto, 2011) ⇒ Andrew G. Barto. (2011). “Adaptive Real-Time Dynamic Programming.” In: (Sammut & Webb, 2011)
11 Adaptive Resonance Theory 23 A Gail A. Carpenter; Stephen Grossberg Bayes Rule ; Bayesian Methods (Gail A. Carpenter; Stephen Grossberg, 2011) ⇒ Gail A. Carpenter; Stephen Grossberg. (2011). “Adaptive Resonance Theory.” In: (Sammut & Webb, 2011)
18 Algorithm Evaluation 36 A Geoffrey I. Webb a Computational Learning Theory ; Model Evaluation (Geoffrey I. Webb, 2011a) ⇒ Geoffrey I. Webb. (2011). “Algorithm Evaluation.” In: (Sammut & Webb, 2011)
22 Ant Colony Optimization 37 A Marco Doigo; Mauro Birattari ACO Swarm Intelligence (Marco Doigo; Mauro Birattari, 2011) ⇒ Marco Doigo; Mauro Birattari. (2011). “Ant Colony Optimization.” In: (Sammut & Webb, 2011)
27 Apriori Algorithm 40 A Hannu Toivonen a Association Rule ; Basket Analysis ; Constraint-Based Mining ; Frequent Itemset ; Frequent Pattern (Hannu Toivonen, 2011a) ⇒ Hannu Toivonen. (2011). “Apriori Algorithm.” In: (Sammut & Webb, 2011)
33 Artificial Immune Systems 41 A Jon Timmis AIS ; Immune computing ; Immune-inspired computing ; Immunocomputing ; Immunological computation (Jon Timmis, 2011) ⇒ Jon Timmis. (2011). “Artificial Immune Systems.” In: (Sammut & Webb, 2011)
36 Artificial Societies 45 A Jurgen Branke Agent-based computational models ; Agent-based modeling and simulation ; Agent-based simulation models Artificial life ; Behavioural cloning ; Co-evolutionary learning ; Multi-agent learning. (Jurgen Branke, 2011) ⇒ Jurgen Branke. (2011). “Artificial Societies.” In: (Sammut & Webb, 2011)
38 Association Rule 49 A Hannu Toivonen b Apriori Algorithm ; Basket Analysis ; Frequent Itemset ; Frequent Pattern (Hannu Toivonen, 2011b) ⇒ Hannu Toivonen. (2011). “Association Rule.” In: (Sammut & Webb, 2011)
40 Associative Reinforcement Learning 50 A Alexander L. Strehl Associative bandit problem ; Bandit problem with side information ; bandit problem with side observation ; One-step reinforcement learning (Alexander L. Strehl, 2011) ⇒ Alexander L. Strehl. (2011). “Associative Reinforcement Learning.” In: (Sammut & Webb, 2011)
41 Attribute 52 A Chris Drummond a Characteristic ; Feature ; Property ; Trait (Chris Drummond, 2011a) ⇒ Chris Drummond. (2011). “Attribute.” In: (Sammut & Webb, 2011)
45 Autonomous Helicopter Flight Using Reinforcement Learning 54 A Adam Coates; Pieter Abbeel; Andrew Y. Ng Apprenticeship Learning ; Reinforcement Learning ; Reward Shaping ([[Adam Coates; Pieter Abbeel; Andrew Y. Ng, 2011]]) ⇒ Adam Coates; Pieter Abbeel; Andrew Y. Ng. (2011). “Autonomous Helicopter Flight Using Reinforcement Learning.” In: (Sammut & Webb, 2011)
48 Average One-Dependence Estimators 63 A Fei Zheng; Geoffrey I. Webb a AODE Bayesian Network ; Naïve Bayes ; Semi-Naïve Bayesian Learning ; Tree-Augmented Naïve Bayes ([[Fei Zheng; Geoffrey I. Webb, 2011a]]) ⇒ Fei Zheng; Geoffrey I. Webb. (2011). “Average One-Dependence Estimators.” In: (Sammut & Webb, 2011)
50 Average-Reward Reinforcement Learning 64 A Prasad Tadepalli ARL ; Average-cost neuro-dynamic programming ; Average cost optimization ; Average-payoff reinforcement learning Efficient Exploration in Reinforcement Learning ; Hierarchical Reinforcement Learning ; Model-Based Reinforcement Learning (Prasad Tadepalli, 2011) ⇒ Prasad Tadepalli. (2011). “Average-Reward Reinforcement Learning.” In: (Sammut & Webb, 2011)
53 Backpropagation 69 A Paul Munro Backprop ; BP ; Generalized delta rule Artificial Neural Networks (Paul Munro, 2011) ⇒ Paul Munro. (2011). “Backpropagation.” In: (Sammut & Webb, 2011)
59 Basket Analysis 74 A Hannu Toivonen c Market basket analysis Apriori Algorithm ; Association Rule ; Frequent Itemset ; Frequent Pattern (Hannu Toivonen, 2011c) ⇒ Hannu Toivonen. (2011). “Basket Analysis.” In: (Sammut & Webb, 2011)
64 Bayes Rule 74 A Geoffrey I. Webb b Bayesian Methods ; Bayesian Network ; Naïve Bayes ; Semi-Naïve Bayesian Learning (Geoffrey I. Webb, 2011b) ⇒ Geoffrey I. Webb. (2011). “Bayes Rule.” In: (Sammut & Webb, 2011)
65 Bayesian Methods 75 A Wray Buntine Bayes Rule ; Bayesian nonparametric Models ; Markov Chain Monte Carlo ; Prior Probability (Wray Buntine, 2011) ⇒ Wray Buntine. (2011). “Bayesian Methods.” In: (Sammut & Webb, 2011)
68 Bayesian Nonparametric Models 81 A Peter Orbanz; Yee Whye The Bayesian methods ; Dirichlet process ; Gaussian processes ; Prior probabilities Bayesian Methods ; Dirichlet Processes ; Gaussian Processes ; Mixture Modelling ; Prior Probabilities (Peter Orbanz; Yee Whye The, 2011) ⇒ Peter Orbanz; Yee Whye The. (2011). “Bayesian Nonparametric Models.” In: (Sammut & Webb, 2011)
69 Bayesian Reinforcement Learning 90 A Pascal Poupart Adaptive control processes ; Bayes adaptive Markov decision processes Dual control ; Optimal learning Active Learning ; Markov Decision Processes, Reinforcement Learning (Pascal Poupart, 2011) ⇒ Pascal Poupart. (2011). “Bayesian Reinforcement Learning.” In: (Sammut & Webb, 2011)
70 Beam Search 93 A Claude Sammut Learning as Search (Claude Sammut, 2011) ⇒ Claude Sammut. (2011). “Beam Search.” In: (Sammut & Webb, 2011)
71 Behavioral Cloning 93 A Claude Sammut a Apprenticeship learning ; Behavioral cloning ; Learning by demonstration ; Learning by imitation ; Learning control rules Apprenticeship Learning ; Inverse Reinforcement Learning ; Learning by Imitation ; Locally Weighted Regression ; Model Trees ; Reinforcement Learning ; System Identification. (Claude Sammut, 2011a) ⇒ Claude Sammut. (2011). “Behavioral Cloning.” In: (Sammut & Webb, 2011)
75 Bias Specific Language 98 A Hendrik Blockeel a Hypothesis Language ; Inductive Logic-Programming (Hendrik Blockeel, 2011a) ⇒ Hendrik Blockeel. (2011). “Bias Specific Language.” In: (Sammut & Webb, 2011)
77 Bias-Variance Trade-offs; Novel Applications 101 A Dev Rajnarayan; David Wolpert (Dev Rajnarayan; David Wolpert, 2011) ⇒ Dev Rajnarayan; David Wolpert. (2011). “Bias-Variance Trade-offs; Novel Applications.” In: (Sammut & Webb, 2011)
82 Biological Learning: Synaptic Plasticity, Hebb Rule and Spike Timing Dependent Plasticity 111 A Wulfram Gerstner Correlation-based learning ; Hebb rule ; Hebbian learning Dimensionality Reduction ; Reinforcement Learning ; self-Organizing Maps (Wulfram Gerstner, 2011) ⇒ Wulfram Gerstner. (2011). Biological Learning: Synaptic Plasticity, Hebb Rule and Spike Timing Dependent Plasticity In: (Sammut & Webb, 2011)
83 Biomedical Informatics 114 A C. David Page; Sriraam Natarajan Learning Models of Biological Sequences (C. David Page; Sriraam Natarajan, 2011) ⇒ C. David Page; Sriraam Natarajan. (2011). “Biomedical Informatics.” In: (Sammut & Webb, 2011)
85 Boltzmann Machine 132 A Geoffrey Hinton a Boltzmann machines (Geoffrey Hinton, 2011a) ⇒ Geoffrey Hinton. (2011). “Boltzmann Machine.” In: (Sammut & Webb, 2011)
96 Cascade-Correlation 139 A Thomas R. Shultz; Scott E. Fahlman Cascor ; CC Artificial Neural Networks ; Backpropagation (Thomas R. Shultz; Scott E. Fahlman, 2011) ⇒ Thomas R. Shultz; Scott E. Fahlman. (2011). “Cascade-Correlation.” In: (Sammut & Webb, 2011)
101 Case-Based Reasoning 147 A Susan Craw a CBR ; Experience-based reasoning ; Lessons-learned systems ; Memory-based learning Explanation-Based Learning ; Instance-Based Learning ; Lazy Learning ; Nearest Neighbor ; Similarity Metrics (Susan Craw, 2011a) ⇒ Susan Craw. (2011). “Case-Based Reasoning.” In: (Sammut & Webb, 2011)
103 Categorical Data Clustering 154 A Periklis Andritsos; Panayiotis Tsaparas Clustering of nonnumerical data ; Grouping Clustering ; Data Mining ; Graph Clustering ; Instance-Based Learning ; Partitional clustering (Periklis Andritsos; Panayiotis Tsaparas, 2011) ⇒ Periklis Andritsos; Panayiotis Tsaparas. (2011). “Categorical Data Clustering.” In: (Sammut & Webb, 2011)
107 Causality 159 A Ricardo Silva Graphical Models ; Learning Graphical Models (Ricardo Silva, 2011) ⇒ Ricardo Silva. (2011). “Causality.” In: (Sammut & Webb, 2011)
113 Class 166 A Chris Drummond b Category ; Class ; Collection ; Kind ; Set ; Sort ; Type (Chris Drummond, 2011b) ⇒ Chris Drummond. (2011). “Class.” In: (Sammut & Webb, 2011)
114 Class Imbalance Problem 167 A Charles X. Ling; Victor S. Sheng (Charles X. Ling; Victor S. Sheng, 2011) ⇒ Charles X. Ling; Victor S. Sheng. (2011). “Class Imbalance Problem.” In: (Sammut & Webb, 2011)
115 Classification 168 A Chris Drummond c Categorization ; Generalization ; Identification ; Induction ; Recognition (Chris Drummond, 2011c) ⇒ Chris Drummond. (2011). “Classification.” In: (Sammut & Webb, 2011)
118 Classifier Systems 172 A Pier Luca Lanzi Genetics-based machine learning ; Learning classifier systems Credit Assignment ; Genetic Algorithms ; Reinforcement Learning ; Rule Learning (Pier Luca Lanzi, 2011) ⇒ Pier Luca Lanzi. (2011). “Classifier Systems.” In: (Sammut & Webb, 2011)
130 Clustering from Data Streams 180 A Joao Gama (Joao Gama, 2011) ⇒ Joao Gama. (2011). “Clustering from Data Streams.” In: (Sammut & Webb, 2011)
140 Coevolutionary Learning 184 A Paul Wiegand Coevolution ; Coevolutionary computation Evolutionary Algorithms (Paul Wiegand, 2011) ⇒ Paul Wiegand. (2011). “Coevolutionary Learning.” In: (Sammut & Webb, 2011)
143 Collective Classification 189 A Prithviraj Sen; Galileo Namata; Mustafa Bilgic; Lise Getoor Interactive classification ; Link-based classification Decision Trees ; Inductive Logic Programming ; Learning from Structured Data ; Relational Learning ; Semi-Supervised Learning ; Statistical Relational Learning (Prithviraj Sen; Galileo Namata; Mustafa Bilgic; Lise Getoor, 2011) ⇒ Prithviraj Sen; Galileo Namata; Mustafa Bilgic; Lise Getoor. (2011). “Collective Classification.” In: (Sammut & Webb, 2011)
151 Complexity in Adaptive Systems 194 A Jun He Adaptive system ; Complex adaptive system (Jun He, 2011) ⇒ Jun He. (2011). “Complexity in Adaptive Systems.” In: (Sammut & Webb, 2011)
152 Complexity of Inductive Inference 198 A Sanjay Jain; Frank Stephan a (Sanjay Jain; Frank Stephan, 2011a) ⇒ Sanjay Jain; Frank Stephan. (2011). “Complexity of Inductive Inference.” In: (Sammut & Webb, 2011)
156 Concept Drift 202 A Claude Sammut; Michael Harries Context-sensitive learning ; Learning with hidden context. Decision Trees ; Ensemble Methods ; Incremental Learning ; Inductive Logic Programming ; Lazy Learning (Claude Sammut; Michael Harries, 2011) ⇒ Claude Sammut; Michael Harries. (2011). “Concept Drift.” In: (Sammut & Webb, 2011)
157 Concept Learning 205 A Claude Sammut b Categorization ; Classification learning Data Mining ; Decision Tree Learning ; Inductive Logic Programming ; Relational Learning ; Rule Learning (Claude Sammut, 2011b) ⇒ Claude Sammut. (2011). “Concept Learning.” In: (Sammut & Webb, 2011)
160 Confusion Matrix 209 A Kai Ming Ting a (Kai Ming Ting, 2011a) ⇒ Kai Ming Ting. (2011). “Confusion Matrix.” In: (Sammut & Webb, 2011)
161 Conjunctive Normal Form 209 A Bernhard Pfahringer a (Bernhard Pfahringer, 2011a) ⇒ Bernhard Pfahringer. (2011). “Conjunctive Normal Form.” In: (Sammut & Webb, 2011)
163 Connections Between Inductive Inference and Machine Learning 210 A John Case; Sanjay Jain Behavioural Cloning ; Clustering ; Concept Drift ; Inductive Logic Programming ; Transfer Learning (John Case; Sanjay Jain, 2011) ⇒ John Case; Sanjay Jain. (2011). “Connections Between Inductive Inference and Machine Learning.” In: (Sammut & Webb, 2011)
166 Constrained Clustering 220 A Kiri L. Wagstaff (Kiri L. Wagstaff, 2011) ⇒ Kiri L. Wagstaff. (2011). “Constrained Clustering.” In: (Sammut & Webb, 2011)
167 Constraint-Based Mining 221 A Siegfried Nijssen a Constrained Clustering ; Frequent Pattern Mining ; Graph Mining ; Tree Mining (Siegfried Nijssen, 2011a) ⇒ Siegfried Nijssen. (2011). “Constraint-Based Mining.” In: (Sammut & Webb, 2011)
179 Correlation Clustering 227 A Anthony Wirth Clustering with advice ; Clustering with constraints ; Clustering with qualitative information ; Clustering with side information (Anthony Wirth, 2011) ⇒ Anthony Wirth. (2011). “Correlation Clustering.” In: (Sammut & Webb, 2011)
184 Cost-sensitive Learning 231 A Charles X. Ling; Victor S. Sheng. Cost-sensitive classification ; Learning with different classification costs (Charles X. Ling; Victor S. Sheng., 2011) ⇒ Charles X. Ling; Victor S. Sheng.. (2011). “Cost-sensitive Learning.” In: (Sammut & Webb, 2011)
186 Covariance Matrix 235 A Xinhua Zhang a Gaussian Distribution ; Gaussian Processes ; Kernel Methods (Xinhua Zhang, 2011a) ⇒ Xinhua Zhang. (2011). “Covariance Matrix.” In: (Sammut & Webb, 2011)
188 Credit Assignment 238 A Claude Sammut c Structural credit assignment ; Temporal credit assignment Bayesian Network ; Classifier Systems ; Generic Algorithms (Claude Sammut, 2011c) ⇒ Claude Sammut. (2011). “Credit Assignment.” In: (Sammut & Webb, 2011)
192 Cross-Lingual Text Mining 243 A Nicola Cancedda, Jean-Michel Renders (Nicola Cancedda, Jean-Michel Renders, 2011) ⇒ Nicola Cancedda, Jean-Michel Renders. (2011). “Cross-Lingual Text Mining.” In: (Sammut & Webb, 2011)
194 Cumulative Learning 249 A Pietro Michelucci; Daniel Oblinger Continual learning ; Lifelong learning ; Sequential inductive transfer (Pietro Michelucci; Daniel Oblinger, 2011) ⇒ Pietro Michelucci; Daniel Oblinger. (2011). “Cumulative Learning.” In: (Sammut & Webb, 2011)
195 Curse of Dimensionality 257 A Eamonn Keogh; Abdullah Mueen (Eamonn Keogh; Abdullah Mueen, 2011) ⇒ Eamonn Keogh; Abdullah Mueen. (2011). “Curse of Dimensionality.” In: (Sammut & Webb, 2011)
198 Data Preparation 259 A Geoffrey I. Webb c Data preprocessing ; Feature construction Data Set ; Discretization ; Entity Resolution ; Evolutionary Feature Selection and Construction ; Feature Construction and Text Mining ; Kernel Methods ; Measurement Scales ; Missing Values ; Noise ; Principal Component Analysis ; Propositionalization (Geoffrey I. Webb, 2011c) ⇒ Geoffrey I. Webb. (2011). “Data Preparation.” In: (Sammut & Webb, 2011)
203 Decision List 261 A Johannes Furnkranz a Ordered rule set Classification Rule ; Disjunctive Normal Form ; Rule Learning (Johannes Furnkranz, 2011a) ⇒ Johannes Furnkranz. (2011). “Decision List.” In: (Sammut & Webb, 2011)
204 Decision Lists and Decision Trees 261 A Johannes Furnkranz b Covering Algorithm ; Decision Trees ; Divide-and-Conquer Learning ; Rule Learning (Johannes Furnkranz, 2011b) ⇒ Johannes Furnkranz. (2011). “Decision Lists and Decision Trees.” In: (Sammut & Webb, 2011)
208 Decision Tree A Johannes Furnkranz c C4:5 ; CART ; Classification tree Decision List ; Decision Lists and Decision Trees ; Decision Stump ; Divide-and-Conquer Learning ; Model Tree ; Pruning ; Regression ; Rule Learning (Johannes Furnkranz, 2011c) ⇒ Johannes Furnkranz. (2011). “Decision Tree.” In: (Sammut & Webb, 2011)
212 Deep Belief Nets 267 A Geoffrey Hinton b Deep belief networks (Geoffrey Hinton, 2011b) ⇒ Geoffrey Hinton. (2011). “Deep Belief Nets.” In: (Sammut & Webb, 2011)
214 Density Estimation 270 A Claude Sammut d Kernel density estimation Kernel Methods ; Locally weighted Regression for Control ; Mixture of Models ; Nearest Neighbor ; Support Vector Machine (Claude Sammut, 2011d) ⇒ Claude Sammut. (2011). “Density Estimation.” In: (Sammut & Webb, 2011)
215 Density-Based Clustering 270 A Joerg Sander Estimation of density level sets ; Mode analysis ; Non-parametric cluster analysis Clustering ; Density Estimation (Joerg Sander, 2011) ⇒ Joerg Sander. (2011). “Density-Based Clustering.” In: (Sammut & Webb, 2011)
220 Dimensionality Reduction 274 A Michail Vlachos a Feature extraction Curse of Dimensionality ; Feature Selection (Michail Vlachos, 2011a) ⇒ Michail Vlachos. (2011). “Dimensionality Reduction.” In: (Sammut & Webb, 2011)
223 Dirichlet Process 280 A Yee Whye The Bayesian Methods ; Bayesian Nonparametrics ; Clustering ; Density Estimation ; Gaussian Process ; Prior Probabilities (Yee Whye The, 2011) ⇒ Yee Whye The. (2011). “Dirichlet Process.” In: (Sammut & Webb, 2011)
225 Discretization 287 A Ying Yang a Binning (Ying Yang, 2011a) ⇒ Ying Yang. (2011). “Discretization.” In: (Sammut & Webb, 2011)
227 Disjunctive Normal Form 289 A Bernhard Pfahringer b (Bernhard Pfahringer, 2011b) ⇒ Bernhard Pfahringer. (2011). “Disjunctive Normal Form.” In: (Sammut & Webb, 2011)
234 Document Classification 289 A Dunja Mladeni; Janez Brank; Marko Grobelnik Document categorization ; Supervised learning on text data Classification ; Document Clustering ; Feature Selection ; Perception ; Semi-Supervised Text Processing ; Support Vector Machine ; Text Visualization ([[Dunja Mladeni; Janez Brank; Marko Grobelnik, 2011]]) ⇒ Dunja Mladeni; Janez Brank; Marko Grobelnik. (2011). “Document Classification.” In: (Sammut & Webb, 2011)
235 Document Clustering 293 A Ying Zhao; George Karypis High-dimensional clustering ; Text clustering ; Unsupervised learning on document datasets Clustering ; Information Retrieval ; Text Mining ; Unsupervised Learning ([[Ying Zhao; George Karypis, 2011]]) ⇒ Ying Zhao; George Karypis. (2011). “Document Clustering.” In: (Sammut & Webb, 2011)
240 Dynamic Memory Model 298 A Susan Craw b Dynamic Memory Model ; Memory organization packets Case-Based Reasoning (Susan Craw, 2011b) ⇒ Susan Craw. (2011). “Dynamic Memory Model.” In: (Sammut & Webb, 2011)
241 Dynamic Programming 298 A Martin L. Puterman, Jonathan Patrick Markov Decision Processes ; Partially Observable Markov Decision Processes (Martin L. Puterman, Jonathan Patrick, 2011) ⇒ Martin L. Puterman, Jonathan Patrick. (2011). “Dynamic Programming.” In: (Sammut & Webb, 2011)
249 Efficient Exploration in Reinforcement Learning 309 A John Langford PAC-MDP learning k Armed Bandit ; Reinforcement Learning (John Langford, 2011) ⇒ John Langford. (2011). “Efficient Exploration in Reinforcement Learning.” In: (Sammut & Webb, 2011)
255 Empirical Risk Minimization 312 A Xinhua Zhang b (Xinhua Zhang, 2011b) ⇒ Xinhua Zhang. (2011). “Empirical Risk Minimization.” In: (Sammut & Webb, 2011)
256 Ensemble Learning 312 A Gavin Brown Committee machines ; Multiple classifier systems (Gavin Brown, 2011) ⇒ Gavin Brown. (2011). “Ensemble Learning.” In: (Sammut & Webb, 2011)
258 Entity Resolution 321 A Indrajit Bhattacharya; Lise Getoor Co-reference resolution ; Deduplication ; Duplicate detection ; Identity uncertainty ; Merge-purge ; Object consolidation ; Record linkage ; Reference reconciliation Classification ; Data Preparation ; Graph Clustering ; Similarity Metrics ; Statistical Relational Learning (Indrajit Bhattacharya; Lise Getoor, 2011) ⇒ Indrajit Bhattacharya; Lise Getoor. (2011). “Entity Resolution.” In: (Sammut & Webb, 2011)
260 Epsilon Covers 326 A Thomas Zeugmann a Statistical Machine Learning ; Support Vector Machines (Thomas Zeugmann, 2011a) ⇒ Thomas Zeugmann. (2011). “Epsilon Covers.” In: (Sammut & Webb, 2011)
261 Epsilon Nets 326 A Thomas Zeugmann b PAC Learning ; VC Dimension (Thomas Zeugmann, 2011b) ⇒ Thomas Zeugmann. (2011). “Epsilon Nets.” In: (Sammut & Webb, 2011)
262 Equation Discovery 327 A Ljupco Todorovski a Computational discovery of quantitative laws ; Symbolic regression Inductive Process Modeling ; Language Bias ; Learning as Search ; Linear Regression ; Measurement Scales ; Regression ; System Identification (Ljupco Todorovski, 2011a) ⇒ Ljupco Todorovski. (2011). “Equation Discovery.” In: (Sammut & Webb, 2011)
266 Error Rate 331 A Kai Ming Ting b Error Accuracy ; Confusion matrix ; Mean absolute error ; Mean squared error (Kai Ming Ting, 2011b) ⇒ Kai Ming Ting. (2011). “Error Rate.” In: (Sammut & Webb, 2011)
275 Evolutionary Clustering 332 A David Corne; Julia Handl Cluster optimization ; Evolutionary grouping ; Genetic clustering ; Genetic grouping. Clustering ; Feature Selection ; Semi-Supervised Learning (David Corne; Julia Handl, 2011) ⇒ David Corne; Julia Handl. (2011). “Evolutionary Clustering.” In: (Sammut & Webb, 2011)
277 Evolutionary Computation in Economics 337 A Serafin Martinez-Jaramillo; Biliana Alexandrova-Kabadjova; Alma Lilia Garcia-Almanza; Tonatiuh Pena Centeno a Evolutionary Algorithms ; Evolutionary Computation in Finance ; Evolutionary Computational Techniques in Marketing ; Genetic Algorithms ; Genetic Programming (Serafin Martinez-Jaramillo; Biliana Alexandrova-Kabadjova; Alma Lilia Garcia-Almanza; Tonatiuh Pena Centeno, 2011a) ⇒ Serafin Martinez-Jaramillo; Biliana Alexandrova-Kabadjova; Alma Lilia Garcia-Almanza; Tonatiuh Pena Centeno. (2011). “Evolutionary Computation in Economics.” In: (Sammut & Webb, 2011)
278 Evolutionary Computation in Finance 344 A Serafin Martinez-Jaramillo; Biliana Alexandrova-Kabadjova; Alma Lilia Garcia-Almanza; Tonatiuh Pena Centeno b Evolutionary Algorithms ; Evolutionary Computation in Economics ; Evolutionary Computational Techniques in Marketing ; Genetic Algorithms ; Genetic Programming (Serafin Martinez-Jaramillo; Biliana Alexandrova-Kabadjova; Alma Lilia Garcia-Almanza; Tonatiuh Pena Centeno, 2011b) ⇒ Serafin Martinez-Jaramillo; Biliana Alexandrova-Kabadjova; Alma Lilia Garcia-Almanza; Tonatiuh Pena Centeno. (2011). “Evolutionary Computation in Finance.” In: (Sammut & Webb, 2011)
279 Evolutionary Computational Techniques in Marketing 351 A Alma Lilia Garcia-Almanza; Biliana Alexandrova-Kabadjova; Serafin Martinez- Jaramillo Evolutionary Algorithms ; Evolutionary Computation in Economics ; Evolutionary Computation in Finance ; Genetic Algorithms ; Genetic Programming (Alma Lilia Garcia-Almanza; Biliana Alexandrova-Kabadjova; Serafin Martinez- Jaramillo, 2011) ⇒ Alma Lilia Garcia-Almanza; Biliana Alexandrova-Kabadjova; Serafin Martinez- Jaramillo. (2011). “Evolutionary Computational Techniques in Marketing.” In: (Sammut & Webb, 2011)
283 Evolutionary Feature Selection and Construction 353 A Krzysztof Krawiec EFSC ; Evolutionary constructive induction ; Evolutionary feature selection ; Evolutionary feature synthesis ; Genetic attribute construction ; Genetic feature selection Constructive Induction ; Data Preparation ; Feature Selection (Krzysztof Krawiec, 2011) ⇒ Krzysztof Krawiec. (2011). “Evolutionary Feature Selection and Construction.” In: (Sammut & Webb, 2011)
285 Evolutionary Fuzzy System 357 A Carlos Kavka (Carlos Kavka, 2011) ⇒ Carlos Kavka. (2011). “Evolutionary Fuzzy System.” In: (Sammut & Webb, 2011)
286 Evolutionary Games 362 A Moshe Sipper Evolutionary Computation ; Genetic Algorithms ; Genetic Programming (Moshe Sipper, 2011) ⇒ Moshe Sipper. (2011). “Evolutionary Games.” In: (Sammut & Webb, 2011)
288 Evolutionary Kernel Learning 369 A Christian Igel Neuroevolution (Christian Igel, 2011) ⇒ Christian Igel. (2011). “Evolutionary Kernel Learning.” In: (Sammut & Webb, 2011)
289 Evolutionary Robotics 373 A Phil Husbands Embodied evolutionary learning ; Evolution of agent behaviors ; Evolution of robot control Co-Evolutionary Learning ; Evolutionary Artificial Neural Networks ; Genetic Algorithms ; Robot Learning (Phil Husbands, 2011) ⇒ Phil Husbands. (2011). “Evolutionary Robotics.” In: (Sammut & Webb, 2011)
294 Expectation Maximization Clustering 382 A Xin Jin; Jiawei Han b EM Clustering Expectation Maximization Algorithm ([[Xin Jin; Jiawei Han, 2011b]]) ⇒ Xin Jin; Jiawei Han. (2011). “Expectation Maximization Clustering.” In: (Sammut & Webb, 2011)
295 Expectation Propagation 383 A Tom Heskes EP Gaussian Distribution ; Gaussian Process ; Graphical Models (Tom Heskes, 2011) ⇒ Tom Heskes. (2011). “Expectation Propagation.” In: (Sammut & Webb, 2011)
301 Explanation-Based Learning 388 A Gerald DeJong; Shiau Hong Lim Analytical learning ; Deductive learning ; Utility problem Explanation-Based Learning for Planning ; Speedup Learning (Gerald DeJong; Shiau Hong Lim, 2011) ⇒ Gerald DeJong; Shiau Hong Lim. (2011). “Explanation-Based Learning.” In: (Sammut & Webb, 2011)
302 Explanation-Based Learning for Planning 392 A Subbarao Kambhampati; Sungwook Yoon Explanation-based generalization for planning ; Speedup learning for planning (Subbarao Kambhampati; Sungwook Yoon, 2011) ⇒ Subbarao Kambhampati; Sungwook Yoon. (2011). “Explanation-Based Learning for Planning.” In: (Sammut & Webb, 2011)
308 Feature Construction in Text Mining 397 A Janez Brank; Dunja Mladenić, Marko Grobelnik Feature generation in text mining Document classification ; Feature Selection in Text Mining ; Kernel Methods ; Support Vector Machine ; Text Mining ([[Janez Brank; Dunja Mladenić, Marko Grobelnik, 2011]]) ⇒ Janez Brank; Dunja Mladenić, Marko Grobelnik. (2011). “Feature Construction in Text Mining.” In: (Sammut & Webb, 2011)
311 Feature Selection 402 A Huan Liu Attribute selection ; Feature reduction ; Feature subset selection ; Variable selection ; Variable subset selection Classification ; Clustering ; Cross Validation ; Curse of Dimensionality ; Dimensionality Reduction ; Semi-Supervised Learning (Huan Liu, 2011) ⇒ Huan Liu. (2011). “Feature Selection.” In: (Sammut & Webb, 2011)
312 Feature Selection in Text Mining 406 A Dunja Mladenić a Dimensionality reduction on text via feature selection (Dunja Mladenić, 2011a) ⇒ Dunja Mladenić. (2011). “Feature Selection in Text Mining.” In: (Sammut & Webb, 2011)
316 First-Order Logic 410 A Peter A. Flach a First-order predicate calculus ; First-order predicate logic ; Predicate calculus ; Predicate logic ; Resolution Abduction ; Entailment ; Higher-Order Logic ; Hypothesis Language ; Inductive Logic Programming ; Learning from Structured Data ; Logic Program ; Propositionalization ; Relational Data Mining (Peter A. Flach, 2011a) ⇒ Peter A. Flach. (2011). “First-Order Logic.” In: (Sammut & Webb, 2011)
322 Formal Concept Analysis 416 A Gemma C. Garriga Clustering ; Constraint-Based Mining ; Frequent Itemset Mining (Gemma C. Garriga, 2011) ⇒ Gemma C. Garriga. (2011). “Formal Concept Analysis.” In: (Sammut & Webb, 2011)
323 Frequent Itemset 417 A Hannu Toivonen d Frequent set Apriori Algorithm ; Association Rule ; Constraint-Based Mining ; Frequent Pattern (Hannu Toivonen, 2011d) ⇒ Hannu Toivonen. (2011). “Frequent Itemset.” In: (Sammut & Webb, 2011)
324 Frequent Pattern 418 A Hannu Toivonen e Apriori Algorithm ; Association Rule ; Basket Analysis ; Constraint-Based Mining ; Data Mining ; Frequent Itemset ; Graph Mining ; Knowledge Discovery in Databases ; Tree Mining (Hannu Toivonen, 2011e) ⇒ Hannu Toivonen. (2011). “Frequent Pattern.” In: (Sammut & Webb, 2011)
330 Gaussian Distribution 425 A Xinhua Zhang c Normal distribution Gaussian Processes (Xinhua Zhang, 2011c) ⇒ Xinhua Zhang. (2011). “Gaussian Distribution.” In: (Sammut & Webb, 2011)
331 Gaussian Processes 428 A Novi Quadrianto; Kristian Kersting; Zhoa Xu Expectation propagation ; Kernels ; Laplace estimate ; Nonparametric Bayesian Dirichlet Process (Novi Quadrianto; Kristian Kersting; Zhoa Xu, 2011) ⇒ Novi Quadrianto; Kristian Kersting; Zhoa Xu. (2011). “Gaussian Processes.” In: (Sammut & Webb, 2011)
332 Gaussian Process Reinforcement Learning 439 A Yaakov Engel (Yaakov Engel, 2011) ⇒ Yaakov Engel. (2011). “Gaussian Process Reinforcement Learning.” In: (Sammut & Webb, 2011)
334 Generalization 447 A Claude Sammut e Classification ; Specialization ; Subsumption ; Logic of Generality (Claude Sammut, 2011e) ⇒ Claude Sammut. (2011). “Generalization.” In: (Sammut & Webb, 2011)
335 Generalization Bounds 447 A Mark Reid Inequalities ; Sample complexity Classification ; Empirical Risk Minimization ; Hypothesis Space ; Loss ; Pac Learning ; Regression ; Regularization ; Structural Risk Minimization ; VC Dimension (Mark Reid, 2011) ⇒ Mark Reid. (2011). “Generalization Bounds.” In: (Sammut & Webb, 2011)
339 Generative and Discriminative Learning 454 A Bin Liu; Geoffrey I. Webb Evolutionary Feature Selection and Construction ([[Bin Liu; Geoffrey I. Webb, 2011]]) ⇒ Bin Liu; Geoffrey I. Webb. (2011). “Generative and Discriminative Learning.” In: (Sammut & Webb, 2011)
341 Genetic and Evolutionary Algorithms 456 A Claude Sammut f Evolutionary Algorithms (Claude Sammut, 2011f) ⇒ Claude Sammut. (2011). “Genetic and Evolutionary Algorithms.” In: (Sammut & Webb, 2011)
347 Genetic Programming 457 A Moshe Sipper (Moshe Sipper, 2011) ⇒ Moshe Sipper. (2011). “Genetic Programming.” In: (Sammut & Webb, 2011)
353 Grammatical Interface 458 A Lorenza Saitta; Michele Sebag Grammatical inference ; Grammar learning (Lorenza Saitta; Michele Sebag, 2011) ⇒ Lorenza Saitta; Michele Sebag. (2011). “Grammatical Interface.” In: (Sammut & Webb, 2011)
355 Graph Clustering 459 A Charu C. Aggarwal Minimum cuts ; Network clustering ; Spectral clustering ; Structured data clustering Group Detection ; Partitional Clustering (Charu C. Aggarwal, 2011) ⇒ Charu C. Aggarwal. (2011). “Graph Clustering.” In: (Sammut & Webb, 2011)
356 Graph Kernels 467 A Thomas Gartner; Tamas Horvath; Stefan Wrobel (Thomas Gartner; Tamas Horvath; Stefan Wrobel, 2011) ⇒ Thomas Gartner; Tamas Horvath; Stefan Wrobel. (2011). “Graph Kernels.” In: (Sammut & Webb, 2011)
357 Graph Mining 469 A Deepayan Chakrabarti Graph Theory ; Greedy Search Approach of Graph Mining ; Inductive Database Search Approach of Graph Mining ; Kernel-Based Approach of Graph Mining ; Tree Mining (Deepayan Chakrabarti, 2011) ⇒ Deepayan Chakrabarti. (2011). “Graph Mining.” In: (Sammut & Webb, 2011)
358 Graphical Models 471 A Julian McAuley; Tiberio Caetano; Wray Buntine Bayesian Network ; Expectation Propogation ; Hidden Markov Models ; Markov Random Field ([[Julian McAuley; Tiberio Caetano; Wray Buntine, 2011]]) ⇒ Julian McAuley; Tiberio Caetano; Wray Buntine. (2011). “Graphical Models.” In: (Sammut & Webb, 2011)
359 Graphs 479 A Tommy R. Jensen (Tommy R. Jensen, 2011) ⇒ Tommy R. Jensen. (2011). “Graphs.” In: (Sammut & Webb, 2011)
360 Greedy Search 482 A Claude Sammut g Learning as Search ; Rule Learning (Claude Sammut, 2011g) ⇒ Claude Sammut. (2011). “Greedy Search.” In: (Sammut & Webb, 2011)
361 Greedy Search Approach of Graph Mining 483 A Lawrence Holder Grammatical Inferences (Lawrence Holder, 2011) ⇒ Lawrence Holder. (2011). “Greedy Search Approach of Graph Mining.” In: (Sammut & Webb, 2011)
362 Group Detection 489 A Hossam Sharara; Lise Getoor Communication detection ; Graph clustering ; Modularity detection Graph Clustering ; Graph Mining (Hossam Sharara; Lise Getoor, 2011) ⇒ Hossam Sharara; Lise Getoor. (2011). “Group Detection.” In: (Sammut & Webb, 2011)
370 Hidden Markov Models 493 A Antal van den Bosch HMM Baum-Welch Algorithm ; Bayesian Methods ; Expectation-Maximization Algorithm ; Markov Process ; Viterbi Algorithm (Antal van den Bosch, 2011) ⇒ Antal van den Bosch. (2011). “Hidden Markov Models.” In: (Sammut & Webb, 2011)
371 Hierarchical Reinforcement Learning 495 A Bernhard Hengst Associative Reinforcement Learning ; Average Reward Reinforcement Learning ; Bayesian Reinforcement Learning ; Credit Assignment ; Markov Decision Process ; Model-Based Reinforcement Learning ; Policy Gradient Methods ; Q Learning ; Reinforcement Learning ; Relational Reinforcement Learning ; Structured Induction ; Temporal Difference Learning (Bernhard Hengst, 2011) ⇒ Bernhard Hengst. (2011). “Hierarchical Reinforcement Learning.” In: (Sammut & Webb, 2011)
373 High-Order Logic 502 A John Lloyd First-Order Logic ; Inductive Logic Programming ; Learning from Structured Data ; Propositional Logic (John Lloyd, 2011) ⇒ John Lloyd. (2011). “High-Order Logic.” In: (Sammut & Webb, 2011)
379 Hopfield Network 507 A Risto Miikkulainen a Recurrent associative memory (Risto Miikkulainen, 2011a) ⇒ Risto Miikkulainen. (2011). “Hopfield Network.” In: (Sammut & Webb, 2011)
380 Hypothesis Language 507 A Hendrik Blockeel b Representation language First-Order Logic ; Hypothesis Space ; Inductive Logic Programming ; Observation Language (Hendrik Blockeel, 2011b) ⇒ Hendrik Blockeel. (2011). “Hypothesis Language.” In: (Sammut & Webb, 2011)
381 Hypothesis Space 511 A Hendrik Blockeel c Model Space Bias Specification Language ; Hypothesis Language ; Inductive Logic Programming ; Observation Programming (Hendrik Blockeel, 2011c) ⇒ Hendrik Blockeel. (2011). “Hypothesis Space.” In: (Sammut & Webb, 2011)
394 Incremental Learning 515 A Paul E. Utgoff Active Learning ; Cumulative Learning ; Online Learning (Paul E. Utgoff, 2011) ⇒ Paul E. Utgoff. (2011). “Incremental Learning.” In: (Sammut & Webb, 2011)
396 Induction 519 A James Cussens Abduction ; Bayesian Statistics ; Classification ; Learning from Analogy ; No-Free Lunch Theorems ; Nonmonotonic Logic (James Cussens, 2011) ⇒ James Cussens. (2011). “Induction.” In: (Sammut & Webb, 2011)
399 Inductive Database Approach to Graphmining 522 A Stefan Kramer (Stefan Kramer, 2011) ⇒ Stefan Kramer. (2011). “Inductive Database Approach to Graphmining.” In: (Sammut & Webb, 2011)
400 Inductive Inference 523 A Sanjay Jain Connections Between Inductive Inference and Machine Learning (Sanjay Jain, 2011) ⇒ Sanjay Jain. (2011). “Inductive Inference.” In: (Sammut & Webb, 2011)
404 Inductive Logic Programming 529 A Luc De Raedt a Learning in logic ; Multi-relational data mining ; Relational data mining ; Relational learning Multi-Relational Data Mining (Luc De Raedt, 2011a) ⇒ Luc De Raedt. (2011). “Inductive Logic Programming.” In: (Sammut & Webb, 2011)
405 Inductive Process Modeling 535 A Ljupco Todorovski b Process-based modeling Equation Discovery (Ljupco Todorovski, 2011b) ⇒ Ljupco Todorovski. (2011). “Inductive Process Modeling.” In: (Sammut & Webb, 2011)
407 Inductive Programming 537 A Pierre Flener; Ute Schmid a Example-based programming ; Inductive program synthesis ; Inductive synthesis ; Program synthesis from examples. Explanation-Based Learning ; Inductive Logic Programming ; Programming by Demonstration ; Trace-Based Programming (Pierre Flener; Ute Schmid, 2011a) ⇒ Pierre Flener; Ute Schmid. (2011). “Inductive Programming.” In: (Sammut & Webb, 2011)
409 Inductive Transfer 545 A Ricardo Vilalta; Christopher Giraud-Carrier; Pavel Brazdil; Carlos Soares Transfer of knowledge across domains Metalearning (Ricardo Vilalta; Christopher Giraud-Carrier; Pavel Brazdil; Carlos Soares, 2011) ⇒ Ricardo Vilalta; Christopher Giraud-Carrier; Pavel Brazdil; Carlos Soares. (2011). “Inductive Transfer.” In: (Sammut & Webb, 2011)
417 Instance-Based Learning 549 A Eamonn Keogh a Analogical reasoning ; Case-based learning ; Memory-based ; Nearest Neighbor Methods, Non-parametric Methods (Eamonn Keogh, 2011a) ⇒ Eamonn Keogh. (2011). “Instance-Based Learning.” In: (Sammut & Webb, 2011)
418 Instance-Based Reinforcement 550 A William D. Smart Kernel-based reinforcement learning Curse of Dimensionality ; Instance-Based Learning ; Locally Weighted Learning ; Value-Function Approximation (William D. Smart, 2011) ⇒ William D. Smart. (2011). “Instance-Based Reinforcement.” In: (Sammut & Webb, 2011)
425 Inverse Reinforcement Learning 554 A Pieter Abbeel; Andrew Y. Ng Intent recognition ; Inverse optimal control ; Plan recognition Apprenticeship Learning ; Reinforcement Learning ; Reward Shaping ([[Pieter Abbeel; Andrew Y. Ng, 2011]]) ⇒ Pieter Abbeel; Andrew Y. Ng. (2011). “Inverse Reinforcement Learning.” In: (Sammut & Webb, 2011)
434 k-Armed Bandit 561 A Shie Mannor Multi-armed bandit ; Multi-armed bandit problem Active Learning ; Associative Bandit Problems ; Dynamic Programming ; Machine Learning in Games ; Markov Processes ; PAC Learning ; Reinforcement Learning (Shie Mannor, 2011) ⇒ Shie Mannor. (2011). “k-Armed Bandit.” In: (Sammut & Webb, 2011)
435 K-Means Clustering 563 A Xin Jin; Jiawei Han c ([[Xin Jin; Jiawei Han, 2011c]]) ⇒ Xin Jin; Jiawei Han. (2011). “K-Means Clustering.” In: (Sammut & Webb, 2011)
436 K-Medoids Clustering 564 A Xin Jin; Jiawei Han d ([[Xin Jin; Jiawei Han, 2011d]]) ⇒ Xin Jin; Jiawei Han. (2011). “K-Medoids Clustering.” In: (Sammut & Webb, 2011)
437 K-Way Spectral Clustering 565 A Xin Jin; Jiawei Han e ([[Xin Jin; Jiawei Han, 2011e]]) ⇒ Xin Jin; Jiawei Han. (2011). “K-Way Spectral Clustering.” In: (Sammut & Webb, 2011)
440 Kernel Methods 566 A Xinhua Zhang d Principal Component Analysis ; Support Vector Machine (Xinhua Zhang, 2011d) ⇒ Xinhua Zhang. (2011). “Kernel Methods.” In: (Sammut & Webb, 2011)
455 Lazy Learning A Geoffrey I. Webb d Eager Learning ; Instance-Based Learning ; Locally Weighted Regression for Control ; Online Learning (Geoffrey I. Webb, 2011d) ⇒ Geoffrey I. Webb. (2011). “Lazy Learning.” In: (Sammut & Webb, 2011)
456 Learning as Search 572 A Claude Sammut h Decision Tree Learning ; Generalization ; Induction ; Instance-Based Learning ; Logic of Generality ; Rule Learning ; Subsumption (Claude Sammut, 2011h) ⇒ Claude Sammut. (2011). “Learning as Search.” In: (Sammut & Webb, 2011)
462 Learning Curves in Machine Learning 577 A Claudia Perlich Error curve ; Experience curve ; Improvement curve ; Training Curve Artificial Neural Networks ; Computational Learning Theory ; Decision Tree ; Generalization Performance ; Logistic Regression ; Overfitting (Claudia Perlich, 2011) ⇒ Claudia Perlich. (2011). “Learning Curves in Machine Learning.” In: (Sammut & Webb, 2011)
467 Learning from Structured Data 580 A Tamas Horvath; Stefan Wrobel Learning from complex data ; Learning from non-propositional data ; Learning from nonvectorial data Hypothesis Language ; Inductive Logic Programming ; Observation Language ; Statistical Relational Learning ; Structured Induction (Tamas Horvath; Stefan Wrobel, 2011) ⇒ Tamas Horvath; Stefan Wrobel. (2011). “Learning from Structured Data.” In: (Sammut & Webb, 2011)
468 Learning Graphical Models 584 A Kevin B. Korb Bayesian model averaging ; Causal discovery ; Dynamic bayesian network ; Learning bayesian networks Dimensionality ; Feature Selection ; Graphical Models ; Hidden Markov Models (Kevin B. Korb, 2011) ⇒ Kevin B. Korb. (2011). “Learning Graphical Models.” In: (Sammut & Webb, 2011)
471 Learning Models of Biological Sequences 590 A William Stafford Noble; Christina Leslie (William Stafford Noble; Christina Leslie, 2011) ⇒ William Stafford Noble; Christina Leslie. (2011). “Learning Models of Biological Sequences.” In: (Sammut & Webb, 2011)
482 Linear Discriminant 601 A Novi Quadrianto, Wray L. Buntine a Regression ; Support Vector Machines (Novi Quadrianto, Wray L. Buntine, 2011a) ⇒ Novi Quadrianto, Wray L. Buntine. (2011). “Linear Discriminant.” In: (Sammut & Webb, 2011)
483 Linear Regression 603 A Novi Quadrianto, Wray L. Buntine b Correlation Matrix ; Gaussian Processes ; Regression (Novi Quadrianto, Wray L. Buntine, 2011b) ⇒ Novi Quadrianto, Wray L. Buntine. (2011). “Linear Regression.” In: (Sammut & Webb, 2011)
487 Link Mining and Link Discovery 606 A Lise Getoor Link analysis ; Network analysis Collective Classification ; Entity Resolution ; Graph Clustering ; Graph Mining ; Group Detection ; Inductive Logic Programming ; Link Prediction ; Relational Learning (Lise Getoor, 2011) ⇒ Lise Getoor. (2011). “Link Mining and Link Discovery.” In: (Sammut & Webb, 2011)
488 Link Prediction 609 A Galileo Namata; Lise Getoor Edge prediction ; Relationship extraction Graph Mining ; Statistical Relational Learning (Galileo Namata; Lise Getoor, 2011) ⇒ Galileo Namata; Lise Getoor. (2011). “Link Prediction.” In: (Sammut & Webb, 2011)
493 Locally Sensitive Hashing Based Clustering 613 A Xin Jin; Jiawei Han f ([[Xin Jin; Jiawei Han, 2011f]]) ⇒ Xin Jin; Jiawei Han. (2011). “Locally Sensitive Hashing Based Clustering.” In: (Sammut & Webb, 2011)
494 Locally Weighted Learning 613 A Jo-Anne Ting; Sethu Vijayakumar; Stefan Schaal Kernel shaping ; Lazy learning ; Local distance metric adaptation ; Locally weighted learning ; LWPR ; LWR ; Nonstationary kernels supersmoothing Bias and Variance ; Dimensionality Reduction ; Incremental Learning ; Kernel Function ; Kernel Methods ; Lazy Learning ; Linear Regression ; Mixture Models ; Online Learning ; Overfitting ; Radial Basis Functions ; Regression ; Supervised Learning (Jo-Anne Ting; Sethu Vijayakumar; Stefan Schaal, 2011) ⇒ Jo-Anne Ting; Sethu Vijayakumar; Stefan Schaal. (2011). “Locally Weighted Learning.” In: (Sammut & Webb, 2011)
495 Logic of Generality 624 A Luc De Raedt b Generality and logic ; Induction as inverted deduction ; Inductive inference rules ; Is more general than ; Is more specific than ; Specialization (Luc De Raedt, 2011b) ⇒ Luc De Raedt. (2011). “Logic of Generality.” In: (Sammut & Webb, 2011)
511 Machine learning and Game Playing 633 A Johannes Furnkranz d Samuel's Checkers Players ; TD-Gammon (Johannes Furnkranz, 2011d) ⇒ Johannes Furnkranz. (2011). “Machine learning and Game Playing.” In: (Sammut & Webb, 2011)
512 Machine Learning for IT Security 637 A Philip K. Chan Association ; Classification (Philip K. Chan, 2011) ⇒ Philip K. Chan. (2011). “Machine Learning for IT Security.” In: (Sammut & Webb, 2011)
513 Manhattan Distance 639 A Susan Craw c City block distance ; L1-distance ; 1-norm distance' Taxi-cab norm distance Case-Based Learning ; Nearest Neighbor (Susan Craw, 2011c) ⇒ Susan Craw. (2011). “Manhattan Distance.” In: (Sammut & Webb, 2011)
518 Markov Chain Monte Carlo 639 A Claude Sammut i MCMC Bayesian Network ; Graphical Models ; Learning Graphical Models ; Markov Chain (Claude Sammut, 2011i) ⇒ Claude Sammut. (2011). “Markov Chain Monte Carlo.” In: (Sammut & Webb, 2011)
519 Markov Decision Processes 642 A William Uther a Policy search Bayesian Network ; Curse of Dimensionality ; Monte-Carol Simulation ; Partially Observable Markov Decision Processes ; Reinforcement Learning ; Temporal Difference Learning (William Uther, 2011a) ⇒ William Uther. (2011). “Markov Decision Processes.” In: (Sammut & Webb, 2011)
527 Maximum Entropy Models for Natural Language Processing 647 A Adwait Ratnaparkhi Log-linear models ; Maxent models ; Statistical natural language processing (Adwait Ratnaparkhi, 2011) ⇒ Adwait Ratnaparkhi. (2011). “Maximum Entropy Models for Natural Language Processing.” In: (Sammut & Webb, 2011)
533 Mean Shift 652 A Xin Jin; Jiawei Han g ([[Xin Jin; Jiawei Han, 2011g]]) ⇒ Xin Jin; Jiawei Han. (2011). “Mean Shift.” In: (Sammut & Webb, 2011)
535 Measurement Scales 653 A Ying Yang b (Ying Yang, 2011b) ⇒ Ying Yang. (2011). “Measurement Scales.” In: (Sammut & Webb, 2011)
536 Medicine: Application of Machine Learning 654 A Katharina Morik Class Imbalance Problem ; Classification ; Classifier Systems ; Cost-Sensitive Learning ; Decision Trees ; Feature Selection ; Inductive Logic Programming ; ROC Analysis ; Support Vector Machine ; Time Series (Katharina Morik, 2011) ⇒ Katharina Morik. (2011). “Medicine: Application of Machine Learning.” In: (Sammut & Webb, 2011)
543 Metaheuristic 662 A Marco Dorigo; Mauro Birattari (Marco Dorigo; Mauro Birattari, 2011) ⇒ Marco Dorigo; Mauro Birattari. (2011). “Metaheuristic.” In: (Sammut & Webb, 2011)
544 Metalearning 662 A Pavel Brazdil, Ricardo Vilalta; Christophe Giraud- Carrier; Carlos Soares Adaptive learning ; Dynamic selection of bias ; Learning to learn ; Ranking learning methods ; self-adaptive systems Inductive Transfer (Pavel Brazdil, Ricardo Vilalta; Christophe Giraud- Carrier; Carlos Soares, 2011) ⇒ Pavel Brazdil, Ricardo Vilalta; Christophe Giraud- Carrier; Carlos Soares. (2011). “Metalearning.” In: (Sammut & Webb, 2011)
546 Minimum Description Length Principle 666 A Jorma Rissanen Information theory ; MDL ; Minimum encoding inference Minimum Message Length (Jorma Rissanen, 2011) ⇒ Jorma Rissanen. (2011). “Minimum Description Length Principle.” In: (Sammut & Webb, 2011)
548 Minimum Message Length 668 A Rohan A. Baxter a Minimum encoding inference Bayesian Methods ; Inductive Inference ; Minimum Description Length (Rohan A. Baxter, 2011a) ⇒ Rohan A. Baxter. (2011). “Minimum Message Length.” In: (Sammut & Webb, 2011)
549 Missing Attribute Values 674 A Ivan Bruha Missing values ; Unknown attribute values ; Unknown values (Ivan Bruha, 2011) ⇒ Ivan Bruha. (2011). “Missing Attribute Values.” In: (Sammut & Webb, 2011)
553 Mixture Model 680 A Rohan A. Baxter b Finite mixture model ; Latent class model ; Mixture distribution ; Mixture modeling Density-Based Clustering ; Density Estimation ; Gaussian Distribution ; Graphical Models ; Learning Graphical Models ; Markov Chain Monte Carlo ; Model-Based Clustering ; Unsupervised Learning (Rohan A. Baxter, 2011b) ⇒ Rohan A. Baxter. (2011). “Mixture Model.” In: (Sammut & Webb, 2011)
556 Model Evaluation 683 A Geoffrey I. Webb e Algorithm Evaluation ; Overfitting ; ROC Analysis (Geoffrey I. Webb, 2011e) ⇒ Geoffrey I. Webb. (2011). “Model Evaluation.” In: (Sammut & Webb, 2011)
559 Model Trees 684 A Luis Torgo Functional trees ; Linear regression trees ; Piecewise linear models Random Forests ; Regression ; Regression Trees ; Supervised Learning ; Training Sample
560 Model-Based Clustering 686 A Arindam Banerjee; Hanhuai Shan (Arindam Banerjee; Hanhuai Shan, 2011) ⇒ Arindam Banerjee; Hanhuai Shan. (2011). “Model-Based Clustering.” In: (Sammut & Webb, 2011)
562 Model-Based Reinforcement Learning 690 A Soumya Ray; Prasad Tadepalli Indirect reinforcement learning Adaptive Real-Time Dynamic Programming ; Autonomous Helicopter Flight Using Reinforcement Learning ; Bayesian Reinforcement Learning ; Efficient Exploration in Reinforcement Learning ; Symbolic Dynamic Programming (Soumya Ray; Prasad Tadepalli, 2011) ⇒ Soumya Ray; Prasad Tadepalli. (2011). “Model-Based Reinforcement Learning.” In: (Sammut & Webb, 2011)
569 Multi-Agent Learning I: Problem Definition 694 A Yoav Shoham; Rob Powers (Yoav Shoham; Rob Powers, 2011) ⇒ Yoav Shoham; Rob Powers. (2011). “Multi-Agent Learning I: Problem Definition.” In: (Sammut & Webb, 2011)
573 MultiBoosting 699 A Geoffrey I. Webb f AdaBoost ; Bagging ; Ensemble Learning ; Multistrategy Ensemble Learning (Geoffrey I. Webb, 2011f) ⇒ Geoffrey I. Webb. (2011). “MultiBoosting.” In: (Sammut & Webb, 2011)
575 Multi-Instance Learning 701 A Soumya Ray; Stephen Scott; Hendrik Blockeel Multiple-instance learning Artificial Neural Network ; Attribute ; Classification ; Data Set ; Decision Tree ; Expectation Maximization ; First-Order Rule ; Gaussian Distribution ; Inductive Logic Programming ; Kernel Methods ; Linear Regression ; Nearest Neighbor ; Noise ; On-Line Learning ; PAC Learning ; Relational Learning ; Supervised Learning ([[Soumya Ray; Stephen Scott; Hendrik Blockeel, 2011]]) ⇒ Soumya Ray; Stephen Scott; Hendrik Blockeel. (2011). “Multi-Instance Learning.” In: (Sammut & Webb, 2011)
583 Naïve Bayes 713 A Geoffrey I. Webb g Idiot's bayes ; Simple bayes Bayes Rule ; Bayesian Method ; Bayesian Networks ; Semi-Naïve Bayesian Learning (Geoffrey I. Webb, 2011g) ⇒ Geoffrey I. Webb. (2011). “Naïve Bayes.” In: (Sammut & Webb, 2011)
586 Nearest Neighbor 714 A Eamonn Keogh b Closest point ; Most similar point (Eamonn Keogh, 2011b) ⇒ Eamonn Keogh. (2011). “Nearest Neighbor.” In: (Sammut & Webb, 2011)
596 Neuroevolution 716 A Risto Miikkulainen b Evolving neural networks ; Genetic neural networks Evolutionary Algorithms ; Reinforcement Learning (Risto Miikkulainen, 2011b) ⇒ Risto Miikkulainen. (2011). “Neuroevolution.” In: (Sammut & Webb, 2011)
597 Neuron 720 A Risto Miikkulainen c Node ; Unit (Risto Miikkulainen, 2011c) ⇒ Risto Miikkulainen. (2011). “Neuron.” In: (Sammut & Webb, 2011)
606 Nonstandard Criteria in Evolutionary Learning 722 A Michele Sebag (Michele Sebag, 2011) ⇒ Michele Sebag. (2011). “Nonstandard Criteria in Evolutionary Learning.” In: (Sammut & Webb, 2011)
616 Observation Language 733 A Hendrik Blockeel d Instance language Hypothesis Language ; Inductive Logic Programming ; Relational Learning (Hendrik Blockeel, 2011d) ⇒ Hendrik Blockeel. (2011). “Observation Language.” In: (Sammut & Webb, 2011)
617 Occam's Razor 736 A Geoffrey I. Webb h Ockham's razor (Geoffrey I. Webb, 2011h) ⇒ Geoffrey I. Webb. (2011). “Occam's Razor.” In: (Sammut & Webb, 2011)
621 Online Learning 736 A Peter Auer Mistake-bounded learning ; Perception ; Prediction with expert advice ; Sequential prediction Incremental Learning (Peter Auer, 2011) ⇒ Peter Auer. (2011). “Online Learning.” In: (Sammut & Webb, 2011)
630 Overfitting 744 A Geoffrey I. Webb i Overtraining Bias and Variance ; Minimum Description Length ; Minimum Message Length ; Pruning ; Regularization (Geoffrey I. Webb, 2011i) ⇒ Geoffrey I. Webb. (2011). “Overfitting.” In: (Sammut & Webb, 2011)
634 PAC Learning 745 A Thomas Zeugmann c Distribution-free learning ; Probably approximately correct learning ; PAC identification Statistical Machine Learning ; Stochastic Finite Learning ; VC Dimension (Thomas Zeugmann, 2011c) ⇒ Thomas Zeugmann. (2011). “PAC Learning.” In: (Sammut & Webb, 2011)
638 Partially Observable Markov Decision Processes 754 A Pascal Poupart POMDPs ; Belief state Markov decision processes ; Dynamic decision networks ; Dual control Markov Decision Process (Pascal Poupart, 2011) ⇒ Pascal Poupart. (2011). “Partially Observable Markov Decision Processes.” In: (Sammut & Webb, 2011)
639 Particle Swarm Optimization 760 A James Kennedy (James Kennedy, 2011) ⇒ James Kennedy. (2011). “Particle Swarm Optimization.” In: (Sammut & Webb, 2011)
640 Partitional Clustering 766 A Xin Jin, Jiawei Han a ([[Xin Jin, Jiawei Han, 2011a]]) ⇒ Xin Jin, Jiawei Han. (2011). “Partitional Clustering.” In: (Sammut & Webb, 2011)
641 Phase Transitions in Machine Learning 767 A Lorenza Saitta; Michelle Sebag Statistical Physics of learning ; Threshold phenomena in learning ; Typical complexity of learning (Lorenza Saitta; Michelle Sebag, 2011) ⇒ Lorenza Saitta; Michelle Sebag. (2011). “Phase Transitions in Machine Learning.” In: (Sammut & Webb, 2011)
646 Policy Gradient Methods 774 A Jan Peters; J. Andrew Bagnell Policy search Markov Decision Process ; Reinforcement Learning ; Value Function Approximation (Jan Peters; J. Andrew Bagnell, 2011) ⇒ Jan Peters; J. Andrew Bagnell. (2011). “Policy Gradient Methods.” In: (Sammut & Webb, 2011)
649 POS Tagging 776 A Walter Daelemans Grammatical tagging ; Morphosyntactic disambiguation ; Part of speech tagging ; Tagging Classification ; Clustering ; Decision Trees ; ILP ; Information Extraction ; Lazy Learning ; Maxent Models ; Text Categorization ; Text Mining (Walter Daelemans, 2011) ⇒ Walter Daelemans. (2011). “POS Tagging.” In: (Sammut & Webb, 2011)
654 Posterior Probability 780 A Geoffrey I. Webb j Posterior Bayesian Methods (Geoffrey I. Webb, 2011j) ⇒ Geoffrey I. Webb. (2011). “Posterior Probability.” In: (Sammut & Webb, 2011)
657 Precision 780 A Kai Ming Ting c Positive predictive value Precision and Recall (Kai Ming Ting, 2011c) ⇒ Kai Ming Ting. (2011). “Precision.” In: (Sammut & Webb, 2011)
658 Precision and Recall 781 A Kai Ming Ting Confusion Matrix
664 Prior Probability 782 A Geoffrey I. Webb k Prior Bayesian Methods (Geoffrey I. Webb, 2011k) ⇒ Geoffrey I. Webb. (2011). “Prior Probability.” In: (Sammut & Webb, 2011)
667 Predictive Techniques in Software Engineering 782 A Jelber Sayyad Shirabad Predictive software models (Jelber Sayyad Shirabad, 2011) ⇒ Jelber Sayyad Shirabad. (2011). “Predictive Techniques in Software Engineering.” In: (Sammut & Webb, 2011)
668 Preference Learning 789 A Johannes Furnkranz; Eyke Hullermeier Learning from preferences Classification ; Meta-Learning ; Rank Correlation (Johannes Furnkranz; Eyke Hullermeier, 2011) ⇒ Johannes Furnkranz; Eyke Hullermeier. (2011). “Preference Learning.” In: (Sammut & Webb, 2011)
674 Privacy-Related Aspects and Techniques 795 A Stan Matwin Privacy-preserving data mining (Stan Matwin, 2011) ⇒ Stan Matwin. (2011). “Privacy-Related Aspects and Techniques.” In: (Sammut & Webb, 2011)
675 Probabilistic Context-Free Grammars 802 A Yasubumi Sakakibara PCFG (Yasubumi Sakakibara, 2011) ⇒ Yasubumi Sakakibara. (2011). “Probabilistic Context-Free Grammars.” In: (Sammut & Webb, 2011)
679 Programming by Demonstration 805 A Pierre Flener; Ute Schmid b Programming by example Inductive Programming ; Trace-Based Programming (Pierre Flener; Ute Schmid, 2011b) ⇒ Pierre Flener; Ute Schmid. (2011). “Programming by Demonstration.” In: (Sammut & Webb, 2011)
682 Protective Clustering 806 A Cecilia M. Procopiuc Local feature selection ; Subspace clustering Clustering ; Curse of Dimensionality ; Data Mining ; Dimensionality Reduction ; k-Means Clustering ; Kernel Methods ; Principal Component Analysis (Cecilia M. Procopiuc, 2011) ⇒ Cecilia M. Procopiuc. (2011). “Protective Clustering.” In: (Sammut & Webb, 2011)
686 Propositionalization 812 A Nicolas Lachiche Attribute ; Feature Construction ; Feature Selection ; Inductive Logic Programming ; Language Bias ; Learning from Structured Data ; Multi-Instance learning ; Relational Learning ; Statistical Relational Learning (Nicolas Lachiche, 2011) ⇒ Nicolas Lachiche. (2011). “Propositionalization.” In: (Sammut & Webb, 2011)
687 Pruning 817 A Johannes Furnkranz e Decision Tree ; Pre-Pruning ; Post-Pruning ; Regularization ; Rule Learning (Johannes Furnkranz, 2011e) ⇒ Johannes Furnkranz. (2011). “Pruning.” In: (Sammut & Webb, 2011)
690 Q-Learning 819 A Peter Stone a Reinforcement Learning ; Temporal Difference Learning (Peter Stone, 2011a) ⇒ Peter Stone. (2011). “Q-Learning.” In: (Sammut & Webb, 2011)
693 Quality Threshold Clustering 819 A Xin Jin; Jiawei Han h QT Clustering ([[Xin Jin; Jiawei Han, 2011h]]) ⇒ Xin Jin; Jiawei Han. (2011). “Quality Threshold Clustering.” In: (Sammut & Webb, 2011)
695 Query-Based Learning 820 A Sanjay Jain; Frank Stephan b (Sanjay Jain; Frank Stephan, 2011b) ⇒ Sanjay Jain; Frank Stephan. (2011). “Query-Based Learning.” In: (Sammut & Webb, 2011)
699 Radial Basis Function Networks 823 A M.D. Buhmann Networks with kernel functions ; Radial basis function approximation ; Radial basis function neural networks ; Regularization networks Artificial Neural Networks ; Regularization ; Support Vector Machines (M.D. Buhmann, 2011) ⇒ M.D. Buhmann. (2011). “Radial Basis Function Networks.” In: (Sammut & Webb, 2011)
712 Recommender Systems 829 A Perm Melville; Vikas Sindhwani (Perm Melville; Vikas Sindhwani, 2011) ⇒ Perm Melville; Vikas Sindhwani. (2011). “Recommender Systems.” In: (Sammut & Webb, 2011)
717 Regression 838 A Novi Quadrianto; Wray I. Buntine Gaussian Processes ; Linear Regression ; Support Vector Machines (Novi Quadrianto; Wray I. Buntine, 2011) ⇒ Novi Quadrianto; Wray I. Buntine. (2011). “Regression.” In: (Sammut & Webb, 2011)
718 Regression Trees 842 A Luis Torgo Decision trees for regression ; Piecewise constant models ; Tree-based regression Model Trees ; Out-of-the-Sample ; Random Forests ; Regression ; Supervised Learning ; Training Sample (Luis Torgo, 2011) ⇒ Luis Torgo. (2011). “Regression Trees.” In: (Sammut & Webb, 2011)
719 Regularization 845 A Xinhua Zhang e Minimum description Length ; Model Evaluation ; Occam's Razor ; Overfitting ; Statistical Learning Theory ; Support Vector Machines ; VC Dimension (Xinhua Zhang, 2011e) ⇒ Xinhua Zhang. (2011). “Regularization.” In: (Sammut & Webb, 2011)
721 Reinforcement Learning 849 A Peter Stone b Associative Reinforcement Learning ; Autonomous Helicopter Flight Using Reinforcement Learning ; Average-Reward Reinforcement Learning ; Bayesian Reinforcement Learning ; Dynamic Programming ; Efficient Exploration in Reinforcement Learning ; Gaussian Process reinforcement Learning ; Hierarchical Reinforcement Learning ; Instance-Based Reinforcement Learning ; Inverse Reinforcement Learning ; Least Squares Reinforcement Learning Methods ; Model-Based Reinforcement Learning ; Policy Gradient Methods ; Q-Learning ; Relational Reinforcement Learning ; Reward Shaping ; Symbolic Dynamic Programming ; Temporal Difference Learning ; Value Function Approximation (Peter Stone, 2011b) ⇒ Peter Stone. (2011). “Reinforcement Learning.” In: (Sammut & Webb, 2011)
726 Relational Learning 851 A Jan Struyf; Hendrik Blockeel Inductive Logic Programming ; Multi-Relational Data Mining ([[Jan Struyf; Hendrik Blockeel, 2011]]) ⇒ Jan Struyf; Hendrik Blockeel. (2011). “Relational Learning.” In: (Sammut & Webb, 2011)
728 Relational Reinforcement Learning 857 A Kurt Driessens Learning in worlds with objects ; Reinforcement learning in structured domains Hierarchical Reinforcement Learning ; Inductive Logic Programming ; Model-Based Reinforcement Learning ; Policy Iteration ; Q-Learning ; Reinforcement Learning ; Relational Learning ; Symbolic Dynamic Programming ; Temporal Difference (Kurt Driessens, 2011) ⇒ Kurt Driessens. (2011). “Relational Reinforcement Learning.” In: (Sammut & Webb, 2011)
733 Reservoir Computing 863 A Risto Miikkulainen d Echo state network ; Liquid state machine (Risto Miikkulainen, 2011d) ⇒ Risto Miikkulainen. (2011). “Reservoir Computing.” In: (Sammut & Webb, 2011)
738 Reward Shaping 863 A Eric Wiewiora Heuristic rewards ; Reward selection Reinforcement Learning (Eric Wiewiora, 2011) ⇒ Eric Wiewiora. (2011). “Reward Shaping.” In: (Sammut & Webb, 2011)
740 Robot Learning 865 A Jan Peters; Russ Tedrake; Nicholas Roy; Jun Morimoto; Behavioral Cloning ; Inverse Reinforcement Learning ; Policy Search ; Reinforcement Learning ; Value Approximation (Jan Peters; Russ Tedrake; Nicholas Roy; Jun Morimoto;, 2011) ⇒ Jan Peters; Russ Tedrake; Nicholas Roy; Jun Morimoto;. (2011). “Robot Learning.” In: (Sammut & Webb, 2011)
741 ROC Analysis 869 A Peter A. Flach b Receiver operating characteristic analysis Accuracy ; Class Imbalance Problem ; Classification ; Confusion Matrix ; Cost-Sensitive Learning ; Error Rate ; False Negative ; False Positive ; Gaussian Distribution ; Posterior Probability ; Precision ; Prior Probability ; Recall ; Sensitivity ; Specificity ; True Negative ; True Positive (Peter A. Flach, 2011b) ⇒ Peter A. Flach. (2011). “ROC Analysis.” In: (Sammut & Webb, 2011)
746 Rule Learning 875 A Johannes Furnkranz f AQ ; Covering algorithm ; CN2 ; Foil ; Laplace estimate ; m-estimate ; OPUS ; RIPPER Apriori Algorithm ; Association Rule ; Decision List ; Decision Trees ; Subgroup Discovery (Johannes Furnkranz, 2011f) ⇒ Johannes Furnkranz. (2011). “Rule Learning.” In: (Sammut & Webb, 2011)
753 Search Engines: Application of ML 882 A Eric Martin Bayesian Methods ; Classification ; Covariance Matrix ; Rule Learning ; Text Mining (Eric Martin, 2011) ⇒ Eric Martin. (2011). “Search Engines: Application of ML.” In: (Sammut & Webb, 2011)
755 Self-Organizing Maps 886 A Samuel Kaski Kohonen maps ; Self-Organizing feature maps ; SOM ART ; Competitive Learning ; Dimensionality Reduction ; Hebbian Learning ; K-means Clustering ; Learning Vector Quantization (Samuel Kaski, 2011) ⇒ Samuel Kaski. (2011). “Self-Organizing Maps.” In: (Sammut & Webb, 2011)
757 Semi-Naïve Bayesian Learning 889 A Fei Zheng; Geoffrey I. Webb b Bayesian Network ; Naïve Bayes ([[Fei Zheng; Geoffrey I. Webb, 2011b]]) ⇒ Fei Zheng; Geoffrey I. Webb. (2011). “Semi-Naïve Bayesian Learning.” In: (Sammut & Webb, 2011)
758 Semi-Supervised Learning 892 A Xiaojin Zhu Co-training ; Learning from labeled and unlabeled data ; Transductive learning Active Learning ; Classification ; Constrained Clustering ; Dimensionality Reduction ; Online Learning ; Regression ; Supervised Learning ; Unsupervised Learning (Xiaojin Zhu, 2011) ⇒ Xiaojin Zhu. (2011). “Semi-Supervised Learning.” In: (Sammut & Webb, 2011)
759 Semi-Supervised Text Processing 897 A Ion Muslea Learning from labeled and unlabeled data ; Transductive learning (Ion Muslea, 2011) ⇒ Ion Muslea. (2011). “Semi-Supervised Text Processing.” In: (Sammut & Webb, 2011)
761 Sensitivity and Specificity 901 A Kai Ming Ting d Confusion Matrix (Kai Ming Ting, 2011d) ⇒ Kai Ming Ting. (2011). “Sensitivity and Specificity.” In: (Sammut & Webb, 2011)
769 Similarity Measures 903 A Michail Vlachos b Distance ; Distance metrics ; Distance functions ; Distance measures Dimensionality Reduction ; Feature Selection (Michail Vlachos, 2011b) ⇒ Michail Vlachos. (2011). “Similarity Measures.” In: (Sammut & Webb, 2011)
781 Speedup Learning 907 A Alan Fern Explanation-Based Learning (Alan Fern, 2011) ⇒ Alan Fern. (2011). “Speedup Learning.” In: (Sammut & Webb, 2011)
792 Statistical Machine Translation 912 A Miles Osborne SMT (Miles Osborne, 2011) ⇒ Miles Osborne. (2011). “Statistical Machine Translation.” In: (Sammut & Webb, 2011)
794 Statistical Physics of Learning 916 A Luc De Raedt; Kristian Kersting Multi-Relational Data Mining ; Relational Learning (Luc De Raedt; Kristian Kersting, 2011) ⇒ Luc De Raedt; Kristian Kersting. (2011). “Statistical Physics of Learning.” In: (Sammut & Webb, 2011)
795 Stochastic Finite Learning 925 A Thomas Zeugmann d Inductive Inference ; PAC Learning (Thomas Zeugmann, 2011d) ⇒ Thomas Zeugmann. (2011). “Stochastic Finite Learning.” In: (Sammut & Webb, 2011)
801 Structural Risk Minimization 929 A Xinhua Zhang f (Xinhua Zhang, 2011f) ⇒ Xinhua Zhang. (2011). “Structural Risk Minimization.” In: (Sammut & Webb, 2011)
804 Structured Induction 930 A Michael Bain Classifier ; Constructive Induction ; Decision Tree ; Feature Construction ; Predicate Invention ; Rule Learning (Michael Bain, 2011) ⇒ Michael Bain. (2011). “Structured Induction.” In: (Sammut & Webb, 2011)
806 Sublinear Clustering 933 A Artur Czumaj; Christian Sohler (Artur Czumaj; Christian Sohler, 2011) ⇒ Artur Czumaj; Christian Sohler. (2011). “Sublinear Clustering.” In: (Sammut & Webb, 2011)
808 Subsumption 937 A Claude Sammut j Generalization ; Induction ; Learning as Search ; Logic Generality (Claude Sammut, 2011j) ⇒ Claude Sammut. (2011). “Subsumption.” In: (Sammut & Webb, 2011)
810 Supervised Descriptive Rule Induction 938 A Peter Kralj Novak; Nada Lavrac; Geoffrey I. Webb SDRI Apriori ; Association Rule Discovery ; Classification Rule ; Contrast Set Mining ; Emerging Pattern Mining ; Subgroup Discovery ; Supervised Rule Induction. ([[Peter Kralj Novak; Nada Lavrac; Geoffrey I. Webb, 2011]]) ⇒ Peter Kralj Novak; Nada Lavrac; Geoffrey I. Webb. (2011). “Supervised Descriptive Rule Induction.” In: (Sammut & Webb, 2011)
812 Support Vector Machines 941 A Xinhua Zhang g Kernel Methods ; Radial Basis Function Networks (Xinhua Zhang, 2011g) ⇒ Xinhua Zhang. (2011). “Support Vector Machines.” In: (Sammut & Webb, 2011)
814 Symbolic Dynamic Programming 946 A Scott Sanner; Kristian Kersting Dynamic programming for relational domains ; Relational dynamic programming ; Relational value iteration ; SDP Dynamic Programming ; Markov Decision Processes (Scott Sanner; Kristian Kersting, 2011) ⇒ Scott Sanner; Kristian Kersting. (2011). “Symbolic Dynamic Programming.” In: (Sammut & Webb, 2011)
826 Temporal Difference Learning 956 A William Uther b Curse of Dimensionality ; Markov Decision Processes ; Monte-Carlo Simulation ; Reinforcement Learning (William Uther, 2011b) ⇒ William Uther. (2011). “Temporal Difference Learning.” In: (Sammut & Webb, 2011)
834 Text Mining 962 A Dunja Mladenić b Analysis of text ; Data mining ; Text learning Cross-lingual Text Mining ; Feature Construction in Text Mining ; Feature Selection in Text Mining ; Semi-Supervised Text Processing ; Text Mining for Advertising ; Text Mining for News and Blogs Analysis ; Text Mining for the Semantic Web ; Text Mining for Spam Filtering ; Text Visualization (Dunja Mladenić, 2011b) ⇒ Dunja Mladenić. (2011). “Text Mining.” In: (Sammut & Webb, 2011)
835 Text Mining for Advertising 963 A Massimiliano Ciaramita Content match ; Contextual advertising ; Sponsored search ; Web advertising Boosting ; Genetic Programming ; Information Retrieval ; Perception ; SVM ; TF-IDF ; Vector Space Model (Massimiliano Ciaramita, 2011) ⇒ Massimiliano Ciaramita. (2011). “Text Mining for Advertising.” In: (Sammut & Webb, 2011)
836 Text Mining for News and Blogs Analysis 968 A Bettina Berendt (Bettina Berendt, 2011) ⇒ Bettina Berendt. (2011). “Text Mining for News and Blogs Analysis.” In: (Sammut & Webb, 2011)
837 Text Mining for Spam Filtering 972 A Aleksander Kolcz Commercial email filtering ; Junk email filtering ; Spam detection ; Unsolicited commercial email filtering Cost-Sensitive Learning ; Logistic Regression ; Naïve Bayes ; Support Vector Machines ; Text Categorization (Aleksander Kolcz, 2011) ⇒ Aleksander Kolcz. (2011). “Text Mining for Spam Filtering.” In: (Sammut & Webb, 2011)
838 Text Mining for Semantic Web 978 A Marko Grobelnik; Dunja Mladenić Active learning ; Classification ; Document Clustering ; Semisupervised Learning ; Semisupervised Text Processing ; Text Mining ; Text Visualization ([[Marko Grobelnik; Dunja Mladenić, 2011]]) ⇒ Marko Grobelnik; Dunja Mladenić. (2011). “Text Mining for Semantic Web.” In: (Sammut & Webb, 2011)
840 Text Visualization 980 A John Risch; Shawn Bohn; Steve Poteet; Anne Kao; Lesley Quach; Jason Wu Semantic mapping ; Text spatialization ; Topic mapping Dimensional Reduction ; Documents Classification/Clustering ; Feature Selection/Construction ; Information Extraction/Visualization ; Self-Organizing Maps ; Text Preprocessing (John Risch; Shawn Bohn; Steve Poteet; Anne Kao; Lesley Quach; Jason Wu , 2011) ⇒ John Risch; Shawn Bohn; Steve Poteet; Anne Kao; Lesley Quach; Jason Wu . (2011). “Text Visualization.” In: (Sammut & Webb, 2011)
844 Time Series 987 A Eamonn Keogh c Temporal data ; Time sequence ; Trajectory data (Eamonn Keogh, 2011c) ⇒ Eamonn Keogh. (2011). “Time Series.” In: (Sammut & Webb, 2011)
846 Topology of a Neural Network 988 A Risto Miikkulainen e Connectivity ; neural network architecture ; structure (Risto Miikkulainen, 2011e) ⇒ Risto Miikkulainen. (2011). “Topology of a Neural Network.” In: (Sammut & Webb, 2011)
847 Trace-Based Programming 989 A Pierre Flener; Ute Schmid c Programming from traces ; Trace-based programming Inductive Programming ; Programming by Demonstration (Pierre Flener; Ute Schmid, 2011c) ⇒ Pierre Flener; Ute Schmid. (2011). “Trace-Based Programming.” In: (Sammut & Webb, 2011)
858 Tree Augmented Naïve Bayes 990 A Fei Zheng; Geoffrey I. Webb c TAN Averaged One-Dependence Estimators ; Bayesian Network ; Naïve Bayes ; Semi-Naïve Bayesian Learning ([[Fei Zheng; Geoffrey I. Webb, 2011c]]) ⇒ Fei Zheng; Geoffrey I. Webb. (2011). “Tree Augmented Naïve Bayes.” In: (Sammut & Webb, 2011)
859 Tree Mining 991 A Siegfried Nijssen b Constraint-based Mining ; Graph Mining (Siegfried Nijssen, 2011b) ⇒ Siegfried Nijssen. (2011). “Tree Mining.” In: (Sammut & Webb, 2011)
870 Universal Learning Theory 1001 A Marcus Hutter Bayes Rule ; Bayesian Methods ; Bayesian Reinforcement Learning ; Classification ; Data Set ; Discriminative Learning ; Hypothesis Learning ; Inductive Inference ; Loss ; Minimum Description Length ; On-line Learning ; Prior Probability ; Reinforcement Learning ; Time Series (Marcus Hutter, 2011) ⇒ Marcus Hutter. (2011). “Universal Learning Theory.” In: (Sammut & Webb, 2011)
879 Value Function Approximation 1011 A Michail G. Lagoudakis Approximate Dynamic Programming ; Neuro-Dynamic Programming ; Cost-to-go Function Approximation Curse of Dimensionality ; Dynamic Programming ; Feature Selection ; Gaussian Process Reinforcement Learning ; Least-Square Reinforcement Learning Methods ; Q-Learning: Radial Basis Functions ; Reinforcement Learning ; Temporal Difference Learning ; Value Iteration (Michail G. Lagoudakis, 2011) ⇒ Michail G. Lagoudakis. (2011). “Value Function Approximation.” In: (Sammut & Webb, 2011)
884 VC Dimension 1021 A Thomas Zeugmann e Epsilon Nets ; PAC Learning ; Statistical Machine Learning ; Structural Risk Management (Thomas Zeugmann, 2011e) ⇒ Thomas Zeugmann. (2011). “VC Dimension.” In: (Sammut & Webb, 2011)
886 Version Space 1024 A Claude Sammut k Learning as Search ; Noise (Claude Sammut, 2011k) ⇒ Claude Sammut. (2011). “Version Space.” In: (Sammut & Webb, 2011)
889 Weight 1027 A Risto Miikkulainen f Connection strength ; Synaptic efficacy (Risto Miikkulainen, 2011f) ⇒ Risto Miikkulainen. (2011). “Weight.” In: (Sammut & Webb, 2011)
891 Word Sense Disambiguation 1027 A Rada Mihalcea Learning word senses ; Solving semantic ambiguity Semi-supervised Text Processing (Rada Mihalcea, 2011) ⇒ Rada Mihalcea. (2011). “Word Sense Disambiguation.” In: (Sammut & Webb, 2011)