| ID
|
Term
|
Page
|
Type
|
Redirect
|
Author(s)
|
mult alp
|
Synonym
|
Cross References
|
GM-RKB Entry
|
|
| 3
|
Accuracy
|
9
|
N
|
|
|
|
|
Confusion Matrix ; Resubstitution Accuracy
|
(Sammut & Webb, 2011) ⇒ . (2011). “Accuracy.” In: (Sammut & Webb, 2011)
|
|
| 5
|
Actions
|
10
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Actions.” In: (Sammut & Webb, 2011)
|
|
| 8
|
AdaBoost
|
19
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “AdaBoost.” In: (Sammut & Webb, 2011)
|
|
| 13
|
Agent
|
36
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Agent.” In: (Sammut & Webb, 2011)
|
|
| 16
|
Agent-Based Simulation Models
|
36
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Agent-Based Simulation Models.” In: (Sammut & Webb, 2011)
|
|
| 23
|
Anytime Algorithm
|
40
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Anytime Algorithm.” In: (Sammut & Webb, 2011)
|
|
| 28
|
Area Under Curve
|
41
|
N
|
|
|
|
AUC
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Area Under Curve.” In: (Sammut & Webb, 2011)
|
|
| 34
|
Artificial Life
|
45
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Artificial Life.” In: (Sammut & Webb, 2011)
|
|
| 35
|
Artificial Neural Networks
|
45
|
N
|
|
|
|
|
Adaptive Resonance Theory ; Backpropagation ; Biological Learning: Synaptic Plasticity, Hebb Rule and Spike Timing Dependency Plasticity ; Boltzmann Machines ; Cascade Correlation ; Competitive Learning ; Deep Belief Networks ; Evolving Neural Networks ; Hypothesis Language ; Neural Network Topology ; Neuroevolution ; Radial Basis Function Networks ; Reservoir Computing ; Self-Organizing Maps ; Simple Recurrent Networks ; Weights
|
(Sammut & Webb, 2011) ⇒ . (2011). “Artificial Neural Networks.” In: (Sammut & Webb, 2011)
|
|
| 37
|
Assertion
|
49
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Assertion.” In: (Sammut & Webb, 2011)
|
|
| 43
|
Attribute -Value Learning
|
54
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Attribute -Value Learning.” In: (Sammut & Webb, 2011)
|
|
| 54
|
Bagging
|
73
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bagging.” In: (Sammut & Webb, 2011)
|
|
| 55
|
Bake-Off
|
73
|
N
|
|
|
|
|
Algorithm Evaluation
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bake-Off.” In: (Sammut & Webb, 2011)
|
|
| 60
|
Batch Learning
|
74
|
N
|
|
|
|
Offline Learning
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Batch Learning.” In: (Sammut & Webb, 2011)
|
|
| 61
|
Baum-Welch Algorithm
|
74
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Baum-Welch Algorithm.” In: (Sammut & Webb, 2011)
|
|
| 67
|
Bayesian Network
|
81
|
N
|
|
|
|
Bayes Net
|
Graphical Models
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bayesian Network.” In: (Sammut & Webb, 2011)
|
|
| 73
|
Bellman Equation
|
97
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bellman Equation.” In: (Sammut & Webb, 2011)
|
|
| 74
|
Bias
|
97
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bias.” In: (Sammut & Webb, 2011)
|
|
| 76
|
Bias Variance Decomposition
|
100
|
N
|
|
|
|
|
Bias-Variance Trade-offs ; Novel Applications
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bias Variance Decomposition.” In: (Sammut & Webb, 2011)
|
|
| 79
|
Bias-Variance-Covariance Decomposition
|
111
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bias-Variance-Covariance Decomposition.” In: (Sammut & Webb, 2011)
|
|
| 80
|
Bilingual Lexicon Extraction
|
111
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bilingual Lexicon Extraction.” In: (Sammut & Webb, 2011)
|
|
| 84
|
Blog Mining
|
132
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Blog Mining.” In: (Sammut & Webb, 2011)
|
|
| 86
|
Boosting
|
136
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Boosting.” In: (Sammut & Webb, 2011)
|
|
| 87
|
Bootstrap Sampling
|
137
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bootstrap Sampling.” In: (Sammut & Webb, 2011)
|
|
| 88
|
Bottom Clauses
|
137
|
N
|
|
|
|
Saturation ; Starting clause
|
Entailment ; Inductive Logic Programming ; Inverse Entailment ; Logic of Generality
|
(Sammut & Webb, 2011) ⇒ . (2011). “Bottom Clauses.” In: (Sammut & Webb, 2011)
|
|
| 91
|
Breakeven Point
|
137
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Breakeven Point.” In: (Sammut & Webb, 2011)
|
|
| 94
|
Cannot-Link Constraint
|
139
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cannot-Link Constraint.” In: (Sammut & Webb, 2011)
|
|
| 95
|
Candidate-Elimination Algorithm
|
139
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Candidate-Elimination Algorithm.” In: (Sammut & Webb, 2011)
|
|
| 102
|
Categorical Attribute
|
154
|
N
|
|
|
|
Qualitative attribute
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Categorical Attribute.” In: (Sammut & Webb, 2011)
|
|
| 119
|
Clause
|
178
|
N
|
|
|
|
|
First-Order Logic ; Inductive Logic-Programming ; Learning from Structured Data ; Logic Program ; Prolog
|
(Sammut & Webb, 2011) ⇒ . (2011). “Clause.” In: (Sammut & Webb, 2011)
|
|
| 120
|
Clause Learning
|
179
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Clause Learning.” In: (Sammut & Webb, 2011)
|
|
| 121
|
Click-Through Rate (CTR)
|
179
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Click-Through Rate (CTR).” In: (Sammut & Webb, 2011)
|
|
| 122
|
Clonal Selection
|
179
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Clonal Selection.” In: (Sammut & Webb, 2011)
|
|
| 124
|
Cluster Editing
|
179
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cluster Editing.” In: (Sammut & Webb, 2011)
|
|
| 125
|
Cluster Ensembles
|
179
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cluster Ensembles.” In: (Sammut & Webb, 2011)
|
|
| 126
|
Cluster Optimization
|
179
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cluster Optimization.” In: (Sammut & Webb, 2011)
|
|
| 127
|
Clustering
|
180
|
N
|
|
|
|
|
Categorical Data Clustering ; Cluster Editing ; Cluster Ensembles ; Clustering from Data Streams ; Constrained Clustering ; Consensus Clustering ; correlation Clustering ; Cross-Language Document Clustering ; Density-Based Clustering ; Dirichlet Process ; Document Clustering ; Evolutionary Clustering ; Graph Clustering ; k-Means Clustering ; k-Medoids Clustering ; Model-Based Clustering ; Partitional Clustering ; Projective Clustering ; Sublinear Clustering
|
(Sammut & Webb, 2011) ⇒ . (2011). “Clustering.” In: (Sammut & Webb, 2011)
|
|
| 128
|
Clustering Aggregation
|
180
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Clustering Aggregation.” In: (Sammut & Webb, 2011)
|
|
| 129
|
Clustering Ensembles
|
180
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Clustering Ensembles.” In: (Sammut & Webb, 2011)
|
|
| 141
|
Collaborative Filtering
|
189
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Collaborative Filtering.” In: (Sammut & Webb, 2011)
|
|
| 147
|
Comparable Corpus
|
194
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Comparable Corpus.” In: (Sammut & Webb, 2011)
|
|
| 149
|
Competitive Learning
|
194
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Competitive Learning.” In: (Sammut & Webb, 2011)
|
|
| 153
|
Compositional Coevolution
|
201
|
N
|
|
|
|
Cooperative coevolution
|
Coevolutionary Learning
|
(Sammut & Webb, 2011) ⇒ . (2011). “Compositional Coevolution.” In: (Sammut & Webb, 2011)
|
|
| 154
|
Computational Complexity of Learning
|
201
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Computational Complexity of Learning.” In: (Sammut & Webb, 2011)
|
|
| 158
|
Conditional Random Field
|
208
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Conditional Random Field.” In: (Sammut & Webb, 2011)
|
|
| 159
|
Confirmation Theory
|
209
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Confirmation Theory.” In: (Sammut & Webb, 2011)
|
|
| 165
|
Consensus Clustering
|
219
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Consensus Clustering.” In: (Sammut & Webb, 2011)
|
|
| 168
|
Constructive Induction
|
225
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Constructive Induction.” In: (Sammut & Webb, 2011)
|
|
| 170
|
Content-Based Filtering
|
226
|
N
|
|
|
|
Content-based recommending
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Content-Based Filtering.” In: (Sammut & Webb, 2011)
|
|
| 175
|
Continuous Attribute
|
226
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Continuous Attribute.” In: (Sammut & Webb, 2011)
|
|
| 176
|
Contrast Set-Mining
|
226
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Contrast Set-Mining.” In: (Sammut & Webb, 2011)
|
|
| 180
|
Correlation-Based Learning
|
231
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Correlation-Based Learning.” In: (Sammut & Webb, 2011)
|
|
| 181
|
Cost
|
231
|
N
|
|
|
|
|
Loss
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cost.” In: (Sammut & Webb, 2011)
|
|
| 182
|
Cost Function
|
231
|
N
|
|
|
|
|
Loss Function
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cost Function.” In: (Sammut & Webb, 2011)
|
|
| 183
|
Cost-Sensitive Classification
|
231
|
N
|
|
|
|
|
Cost-Sensitive Learning
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cost-Sensitive Classification.” In: (Sammut & Webb, 2011)
|
|
| 189
|
Cross-Language Document Categorization
|
242
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cross-Language Document Categorization.” In: (Sammut & Webb, 2011)
|
|
| 190
|
Cross-Language Information Retrieval
|
242
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cross-Language Information Retrieval.” In: (Sammut & Webb, 2011)
|
|
| 191
|
Cross-Language Question Answering
|
242
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cross-Language Question Answering.” In: (Sammut & Webb, 2011)
|
|
| 193
|
Cross-Validation
|
249
|
N
|
|
|
|
|
Algorithm Evaluation ; Leave-One-Out Cross-Validation
|
(Sammut & Webb, 2011) ⇒ . (2011). “Cross-Validation.” In: (Sammut & Webb, 2011)
|
|
| 200
|
Data Set
|
261
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Data Set.” In: (Sammut & Webb, 2011)
|
|
| 201
|
DBN
|
261
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “DBN.” In: (Sammut & Webb, 2011)
|
|
| 202
|
Decision Epoch
|
261
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Decision Epoch.” In: (Sammut & Webb, 2011)
|
|
| 205
|
Decision Rule
|
262
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Decision Rule.” In: (Sammut & Webb, 2011)
|
|
| 206
|
Decision Stump
|
262
|
N
|
|
|
|
|
Bias and Variance ; Decision Tree ; Overfitting
|
(Sammut & Webb, 2011) ⇒ . (2011). “Decision Stump.” In: (Sammut & Webb, 2011)
|
|
| 207
|
Decision Threshold
|
263
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Decision Threshold.” In: (Sammut & Webb, 2011)
|
|
| 210
|
Deductive Learning
|
267
|
N
|
|
|
|
Analytical learning ; Explanation-based learning
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Deductive Learning.” In: (Sammut & Webb, 2011)
|
|
| 217
|
Detail
|
274
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Detail.” In: (Sammut & Webb, 2011)
|
|
| 219
|
Digraphs
|
274
|
N
|
|
|
|
Directed graphs
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Digraphs.” In: (Sammut & Webb, 2011)
|
|
| 224
|
Discrete Attribute
|
287
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Discrete Attribute.” In: (Sammut & Webb, 2011)
|
|
| 226
|
Discriminative Learning
|
288
|
N
|
|
|
|
|
Generative References
|
(Sammut & Webb, 2011) ⇒ . (2011). “Discriminative Learning.” In: (Sammut & Webb, 2011)
|
|
| 233
|
Divide-and-Conquer Learning
|
289
|
N
|
|
|
|
Recursive partitioning ; TDIDT strategy
|
Covering Algorithm ; Decision Tree
|
(Sammut & Webb, 2011) ⇒ . (2011). “Divide-and-Conquer Learning.” In: (Sammut & Webb, 2011)
|
|
| 243
|
Dynamic Systems
|
308
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Dynamic Systems.” In: (Sammut & Webb, 2011)
|
|
| 254
|
Emerging Patterns
|
312
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Emerging Patterns.” In: (Sammut & Webb, 2011)
|
|
| 257
|
Entailment
|
320
|
N
|
|
|
|
Implications ; Logical consequence
|
Inverse Entailment ; Learning from Entailment ; Logic of Generality
|
(Sammut & Webb, 2011) ⇒ . (2011). “Entailment.” In: (Sammut & Webb, 2011)
|
|
| 267
|
Error Squared
|
331
|
N
|
|
|
|
Squared error
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Error Squared.” In: (Sammut & Webb, 2011)
|
|
| 269
|
Evaluation
|
331
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Evaluation.” In: (Sammut & Webb, 2011)
|
|
| 274
|
Evolutionary Algorithms
|
332
|
N
|
|
|
|
Evolutionary computation ; Evolutionary computing ; Genetic and evolutionary algorithms
|
Coevolution Learning ; Compositional Coevolution ; Evolutionary Clustering ; Evolutionary Computation in Economics ; Evolutionary Computation in Finance ; Evolutionary Computational Techniques in Marketing ; Evolutionary Feature Selection and Construction ; Evolutionary Fuzzy Systems ; Evolutionary Games ; Evolutionary Kernel Learning ; Evolutionary Robotics ; Neuroevolution ; Nonstandard Criteria in Evolutionary Learning ; Test-Based Coevolution
|
(Sammut & Webb, 2011) ⇒ . (2011). “Evolutionary Algorithms.” In: (Sammut & Webb, 2011)
|
|
| 276
|
Evolutionary Computation
|
337
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Evolutionary Computation.” In: (Sammut & Webb, 2011)
|
|
| 296
|
Expectation-Maximization Algorithm
|
387
|
N
|
|
|
|
EM Algorithm ; Expectation Maximization Algorithm
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Expectation-Maximization Algorithm.” In: (Sammut & Webb, 2011)
|
|
| 299
|
Explanation
|
388
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Explanation.” In: (Sammut & Webb, 2011)
|
|
| 304
|
F1-Measure
|
397
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “F1-Measure.” In: (Sammut & Webb, 2011)
|
|
| 305
|
False Negative
|
397
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “False Negative.” In: (Sammut & Webb, 2011)
|
|
| 319
|
First-Order Regression Tree
|
415
|
N
|
|
|
|
Logical regression tree ; Relational regression tree
|
First-Order Rule ; Inductive Logic Programming ; Relational Reinforcement Learning
|
(Sammut & Webb, 2011) ⇒ . (2011). “First-Order Regression Tree.” In: (Sammut & Webb, 2011)
|
|
| 327
|
Fuzzy Sets
|
423
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Fuzzy Sets.” In: (Sammut & Webb, 2011)
|
|
| 328
|
Fuzzy Systems
|
423
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Fuzzy Systems.” In: (Sammut & Webb, 2011)
|
|
| 340
|
Generative Learning
|
455
|
N
|
|
|
|
|
Generative and Discriminative Learning
|
(Sammut & Webb, 2011) ⇒ . (2011). “Generative Learning .” In: (Sammut & Webb, 2011)
|
|
| 349
|
Gibbs Sampling
|
457
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Gibbs Sampling.” In: (Sammut & Webb, 2011)
|
|
| 350
|
Gini Coefficient
|
457
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Gini Coefficient.” In: (Sammut & Webb, 2011)
|
|
| 364
|
Growing Set
|
492
|
N
|
|
|
|
|
Data Set
|
(Sammut & Webb, 2011) ⇒ . (2011). “Growing Set.” In: (Sammut & Webb, 2011)
|
|
| 367
|
Hebb Rule
|
493
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Hebb Rule.” In: (Sammut & Webb, 2011)
|
|
| 368
|
Hebbian Learning
|
493
|
N
|
|
|
|
|
Biological Learning ; Synaptic Plasticity ; Hebb Rule and Spike Timing Dependent Plasticity
|
(Sammut & Webb, 2011) ⇒ . (2011). “Hebbian Learning.” In: (Sammut & Webb, 2011)
|
|
| 377
|
Holdout Evaluation
|
506
|
N
|
|
|
|
|
Algorithm Evaluation
|
(Sammut & Webb, 2011) ⇒ . (2011). “Holdout Evaluation.” In: (Sammut & Webb, 2011)
|
|
| 378
|
Holdout Set
|
507
|
N
|
|
|
|
Holdout Data
|
Evaluation Set ; Holdout Evaluation
|
(Sammut & Webb, 2011) ⇒ . (2011). “Holdout Set.” In: (Sammut & Webb, 2011)
|
|
| 388
|
Immune Network
|
515
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Immune Network.” In: (Sammut & Webb, 2011)
|
|
| 395
|
Indirect Reinforcement Learning
|
519
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Indirect Reinforcement Learning.” In: (Sammut & Webb, 2011)
|
|
| 398
|
Inductive Bias
|
522
|
N
|
|
|
|
Learning bias ; Variance hint
|
Induction ; Learning as Search
|
(Sammut & Webb, 2011) ⇒ . (2011). “Inductive Bias.” In: (Sammut & Webb, 2011)
|
|
| 401
|
Inductive Inference
|
528
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Inductive Inference.” In: (Sammut & Webb, 2011)
|
|
| 403
|
Inductive Learning
|
529
|
N
|
|
|
|
Statistical Learning
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Inductive Learning.” In: (Sammut & Webb, 2011)
|
|
| 408
|
Inductive Synthesis
|
544
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Inductive Synthesis.” In: (Sammut & Webb, 2011)
|
|
| 411
|
Information Retrieval
|
548
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Information Retrieval.” In: (Sammut & Webb, 2011)
|
|
| 413
|
In-Sample Evaluation
|
548
|
N
|
|
|
|
Within-sample evaluation
|
Algorithm Evaluation
|
(Sammut & Webb, 2011) ⇒ . (2011). “In-Sample Evaluation.” In: (Sammut & Webb, 2011)
|
|
| 414
|
Instance
|
549
|
N
|
|
|
|
Case ; Example ; Item ; Object
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Instance.” In: (Sammut & Webb, 2011)
|
|
| 416
|
Instance Space
|
549
|
N
|
|
|
|
Example space ; Item space ; Object space
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Instance Space.” In: (Sammut & Webb, 2011)
|
|
| 419
|
Intelligent Backtracking
|
553
|
N
|
|
|
|
Dependency directed backtracking
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Intelligent Backtracking.” In: (Sammut & Webb, 2011)
|
|
| 421
|
Internal Model Control
|
553
|
N
|
|
|
|
Certainty equivalence principle ; Model-based control
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Internal Model Control.” In: (Sammut & Webb, 2011)
|
|
| 422
|
Interval Scale
|
553
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Interval Scale.” In: (Sammut & Webb, 2011)
|
|
| 423
|
Inverse Entailment
|
553
|
N
|
|
|
|
|
Bottom Clause ; Entailment ; Inductive Logic Programming ; Logic of Generality
|
(Sammut & Webb, 2011) ⇒ . (2011). “Inverse Entailment.” In: (Sammut & Webb, 2011)
|
|
| 426
|
Inverse Resolution
|
558
|
N
|
|
|
|
|
First-Order Logic ; Logic of Generality ; Resolution
|
(Sammut & Webb, 2011) ⇒ . (2011). “Inverse Resolution.” In: (Sammut & Webb, 2011)
|
|
| 439
|
Kernel Matrix
|
566
|
N
|
|
|
|
Gram Matrix
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Kernel Matrix.” In: (Sammut & Webb, 2011)
|
|
| 449
|
Label
|
571
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Label.” In: (Sammut & Webb, 2011)
|
|
| 450
|
Labeled Data
|
571
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Labeled Data.” In: (Sammut & Webb, 2011)
|
|
| 451
|
Language Bias
|
571
|
N
|
|
|
|
|
Learning as Search
|
(Sammut & Webb, 2011) ⇒ . (2011). “Language Bias.” In: (Sammut & Webb, 2011)
|
|
| 454
|
Latent Factor Models and Matrix Factorization
|
571
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Latent Factor Models and Matrix Factorization.” In: (Sammut & Webb, 2011)
|
|
| 472
|
Learning Vector Quantization
|
594
|
N
|
|
|
|
LVQ
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Learning Vector Quantization.” In: (Sammut & Webb, 2011)
|
|
| 477
|
Leave-One-Out Error
|
601
|
N
|
|
|
|
Hold-one-out error ; LOO error
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Leave-One-Out Error.” In: (Sammut & Webb, 2011)
|
|
| 481
|
Lift
|
601
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Lift.” In: (Sammut & Webb, 2011)
|
|
| 485
|
Linear Separability
|
606
|
N
|
|
|
|
|
Perceptrons ; Support Vector Machines
|
(Sammut & Webb, 2011) ⇒ . (2011). “Linear Separability.” In: (Sammut & Webb, 2011)
|
|
| 491
|
Local Distance Metric Adaptation
|
613
|
N
|
|
|
|
Supersmoothing ; Nonstationary kernels ; Kernel shaping
|
Locally Weighted Regression for Control
|
(Sammut & Webb, 2011) ⇒ . (2011). “Local Distance Metric Adaptation.” In: (Sammut & Webb, 2011)
|
|
| 496
|
Logic Program
|
631
|
N
|
|
|
|
|
Clause ; First-Order-Logic
|
(Sammut & Webb, 2011) ⇒ . (2011). “Logic Program.” In: (Sammut & Webb, 2011)
|
|
| 499
|
Logistic Regression
|
631
|
N
|
|
|
|
Logit model
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Logistic Regression.” In: (Sammut & Webb, 2011)
|
|
| 501
|
Log Linear Models
|
632
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Log Linear Models.” In: (Sammut & Webb, 2011)
|
|
| 502
|
Long-Term Potentiation of Synapses
|
632
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Long-Term Potentiation of Synapses.” In: (Sammut & Webb, 2011)
|
|
| 504
|
Loopy Belief Propagation
|
632
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Loopy Belief Propagation.” In: (Sammut & Webb, 2011)
|
|
| 505
|
Loss
|
632
|
N
|
|
|
|
Cost
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Loss.” In: (Sammut & Webb, 2011)
|
|
| 506
|
Loss Function
|
632
|
N
|
|
|
|
Cost Function
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Loss Function.” In: (Sammut & Webb, 2011)
|
|
| 514
|
Margin
|
639
|
N
|
|
|
|
|
Support Vector Machines
|
(Sammut & Webb, 2011) ⇒ . (2011). “Margin.” In: (Sammut & Webb, 2011)
|
|
| 522
|
Markov Network
|
646
|
N
|
|
|
|
Markov net ; Markov random field
|
Graphical Models
|
(Sammut & Webb, 2011) ⇒ . (2011). “Markov Network.” In: (Sammut & Webb, 2011)
|
|
| 523
|
Markov Process
|
646
|
N
|
|
|
|
Markov chain ; Markov model
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Markov Process.” In: (Sammut & Webb, 2011)
|
|
| 525
|
Markovian Decision Rule
|
647
|
N
|
|
|
|
Randomized decision rule
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Markovian Decision Rule.” In: (Sammut & Webb, 2011)
|
|
| 526
|
Maxent Models
|
647
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Maxent Models.” In: (Sammut & Webb, 2011)
|
|
| 528
|
McDiarmids' Inequality
|
651
|
N
|
|
|
|
Bounded differences inequality
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “McDiarmids' Inequality.” In: (Sammut & Webb, 2011)
|
|
| 531
|
Mean Absolute Deviation
|
652
|
N
|
|
|
|
Absolute error loss ; Mean absolute deviation ; Mean error
|
Mean Squared Error
|
(Sammut & Webb, 2011) ⇒ . (2011). “Mean Absolute Deviation.” In: (Sammut & Webb, 2011)
|
|
| 534
|
Mean Squared Error
|
653
|
N
|
|
|
|
Quadratic loss ; Squared error loss
|
Mean Absolute Error
|
(Sammut & Webb, 2011) ⇒ . (2011). “Mean Squared Error.” In: (Sammut & Webb, 2011)
|
|
| 541
|
Message
|
661
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Message.” In: (Sammut & Webb, 2011)
|
|
| 542
|
Meta-Combiner
|
662
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Meta-Combiner.” In: (Sammut & Webb, 2011)
|
|
| 545
|
Minimum Cuts
|
666
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Minimum Cuts.” In: (Sammut & Webb, 2011)
|
|
| 557
|
Model Selection
|
683
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Model Selection.” In: (Sammut & Webb, 2011)
|
|
| 566
|
Most General Hypothesis
|
693
|
N
|
|
|
|
Maximally general hypothesis
|
Learning as Search
|
(Sammut & Webb, 2011) ⇒ . (2011). “Most General Hypothesis.” In: (Sammut & Webb, 2011)
|
|
| 568
|
Most Specific Hypothesis
|
694
|
N
|
|
|
|
Maximally specific hypothesis
|
Learning as Search
|
(Sammut & Webb, 2011) ⇒ . (2011). “Most Specific Hypothesis.” In: (Sammut & Webb, 2011)
|
|
| 570
|
Multi-Agent Learning II: Algorithms
|
696
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Multi-Agent Learning II: Algorithms.” In: (Sammut & Webb, 2011)
|
|
| 576
|
Multi-Objective Optimization
|
710
|
N
|
|
|
|
MOO ; Multi-criteria optimization ; Vector optimization
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Multi-Objective Optimization.” In: (Sammut & Webb, 2011)
|
|
| 578
|
Multiple -Instance Learning
|
711
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Multiple -Instance Learning.” In: (Sammut & Webb, 2011)
|
|
| 579
|
Multi-Relational Data Mining
|
711
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Multi-Relational Data Mining.” In: (Sammut & Webb, 2011)
|
|
| 580
|
Multistrategy Ensemble Learning
|
711
|
N
|
|
|
|
|
Ensemble Learning ; Multi-Boosting ; Random Forests
|
(Sammut & Webb, 2011) ⇒ . (2011). “Multistrategy Ensemble Learning.” In: (Sammut & Webb, 2011)
|
|
| 581
|
Must-Link Constraint
|
711
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Must-Link Constraint.” In: (Sammut & Webb, 2011)
|
|
| 588
|
Negative Correlation Learning
|
715
|
N
|
|
|
|
NC-Learning ; NCL
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Negative Correlation Learning.” In: (Sammut & Webb, 2011)
|
|
| 589
|
Negative Predictive Value
|
715
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Negative Predictive Value.” In: (Sammut & Webb, 2011)
|
|
| 593
|
Neural Networks
|
|
N
|
|
|
|
|
Radial Basis Function Networks
|
(Sammut & Webb, 2011) ⇒ . (2011). “Neural Networks.” In: (Sammut & Webb, 2011)
|
|
| 599
|
No-Free-Lunch Theorem
|
721
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “No-Free-Lunch Theorem.” In: (Sammut & Webb, 2011)
|
|
| 600
|
Nogood Learning
|
721
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Nogood Learning.” In: (Sammut & Webb, 2011)
|
|
| 601
|
Noise
|
721
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Noise.” In: (Sammut & Webb, 2011)
|
|
| 602
|
Nominal Attribute
|
722
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Nominal Attribute.” In: (Sammut & Webb, 2011)
|
|
| 610
|
NP-Completeness
|
731
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “NP-Completeness.” In: (Sammut & Webb, 2011)
|
|
| 611
|
Numeric Attribute
|
732
|
N
|
|
|
|
Quantitative attribute
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Numeric Attribute.” In: (Sammut & Webb, 2011)
|
|
| 622
|
Ontology Learning
|
743
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Ontology Learning.” In: (Sammut & Webb, 2011)
|
|
| 623
|
Opinion Mining
|
743
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Opinion Mining.” In: (Sammut & Webb, 2011)
|
|
| 624
|
Optimal Learning
|
743
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Optimal Learning.” In: (Sammut & Webb, 2011)
|
|
| 625
|
OPUS
|
743
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “OPUS.” In: (Sammut & Webb, 2011)
|
|
| 626
|
Ordered Rule Set
|
743
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Ordered Rule Set.” In: (Sammut & Webb, 2011)
|
|
| 627
|
Ordinal Attribute
|
743
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Ordinal Attribute.” In: (Sammut & Webb, 2011)
|
|
| 628
|
Out-of-Sample Data
|
743
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Out-of-Sample Data.” In: (Sammut & Webb, 2011)
|
|
| 629
|
Out-of-Sample Evaluation
|
743
|
N
|
|
|
|
|
Algorithm Evaluation
|
(Sammut & Webb, 2011) ⇒ . (2011). “Out-of-Sample Evaluation.” In: (Sammut & Webb, 2011)
|
|
| 635
|
PAC-MDP Learning
|
753
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “PAC-MDP Learning.” In: (Sammut & Webb, 2011)
|
|
| 636
|
Parallel Corpus
|
754
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Parallel Corpus.” In: (Sammut & Webb, 2011)
|
|
| 637
|
Part of Speech Tagging
|
754
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Part of Speech Tagging.” In: (Sammut & Webb, 2011)
|
|
| 652
|
Positive Semidefinite
|
779
|
N
|
|
|
|
Positive definite
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Positive Semidefinite.” In: (Sammut & Webb, 2011)
|
|
| 655
|
Post-Pruning
|
780
|
N
|
|
|
|
|
Overfitting ; Pre-Pruning ; Pruning
|
(Sammut & Webb, 2011) ⇒ . (2011). “Post-Pruning.” In: (Sammut & Webb, 2011)
|
|
| 656
|
Postsynaptic Neuron
|
780
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Postsynaptic Neuron.” In: (Sammut & Webb, 2011)
|
|
| 659
|
Predicate
|
781
|
N
|
|
|
|
|
Clause ; First-Order Logic ; Logic Program
|
(Sammut & Webb, 2011) ⇒ . (2011). “Predicate.” In: (Sammut & Webb, 2011)
|
|
| 661
|
Predicate Invention
|
781
|
N
|
|
|
|
|
Inductive Logic Programming ; Logic of Generality
|
(Sammut & Webb, 2011) ⇒ . (2011). “Predicate Invention.” In: (Sammut & Webb, 2011)
|
|
| 669
|
Pre-Pruning
|
795
|
N
|
|
|
|
Stopping criteria
|
Overfitting ; Post-Pruning ; Pruning
|
(Sammut & Webb, 2011) ⇒ . (2011). “Pre-Pruning.” In: (Sammut & Webb, 2011)
|
|
| 670
|
Presynaptic Neuron
|
795
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Presynaptic Neuron.” In: (Sammut & Webb, 2011)
|
|
| 671
|
Principal Component Analysis
|
795
|
N
|
|
|
|
PCA
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Principal Component Analysis.” In: (Sammut & Webb, 2011)
|
|
| 677
|
Process-Based Modeling
|
805
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Process-Based Modeling.” In: (Sammut & Webb, 2011)
|
|
| 683
|
Prolog
|
811
|
N
|
|
|
|
|
Clause ; First-Order Logic ; Inductive Logic Programming ; Logic Program
|
(Sammut & Webb, 2011) ⇒ . (2011). “Prolog.” In: (Sammut & Webb, 2011)
|
|
| 685
|
Propositional Logic
|
812
|
N
|
|
|
|
|
First-Order Logic ; Propositionalization
|
(Sammut & Webb, 2011) ⇒ . (2011). “Propositional Logic.” In: (Sammut & Webb, 2011)
|
|
| 688
|
Pruning Set
|
817
|
N
|
|
|
|
|
Data Set
|
(Sammut & Webb, 2011) ⇒ . (2011). “Pruning Set.” In: (Sammut & Webb, 2011)
|
|
| 697
|
Rademacher Complexity
|
823
|
N
|
|
|
|
Rademacher average
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Rademacher Complexity.” In: (Sammut & Webb, 2011)
|
|
| 702
|
Random Forests
|
828
|
N
|
|
|
|
Random decision forests
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Random Forests.” In: (Sammut & Webb, 2011)
|
|
| 703
|
Random Subspace Method
|
828
|
N
|
|
|
|
Random subspaces ; RSM
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Random Subspace Method.” In: (Sammut & Webb, 2011)
|
|
| 706
|
Rank Correlation
|
828
|
N
|
|
|
|
|
Preference Learning ; ROC Analysis
|
(Sammut & Webb, 2011) ⇒ . (2011). “Rank Correlation.” In: (Sammut & Webb, 2011)
|
|
| 707
|
Ratio Scale
|
828
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Ratio Scale.” In: (Sammut & Webb, 2011)
|
|
| 708
|
Real-Time Dynamic Programming
|
829
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Real-Time Dynamic Programming.” In: (Sammut & Webb, 2011)
|
|
| 709
|
Recall
|
829
|
N
|
|
|
|
|
Sensitivity
|
(Sammut & Webb, 2011) ⇒ . (2011). “Recall.” In: (Sammut & Webb, 2011)
|
|
| 723
|
Relational
|
851
|
N
|
|
|
|
|
Propositionalization ; Relational Data Mining ; Relational Learning ; Relational Learning
|
(Sammut & Webb, 2011) ⇒ . (2011). “Relational .” In: (Sammut & Webb, 2011)
|
|
| 731
|
Relevance Feedback
|
863
|
N
|
|
|
|
|
Search Engines ; Applications of ML
|
(Sammut & Webb, 2011) ⇒ . (2011). “Relevance Feedback.” In: (Sammut & Webb, 2011)
|
|
| 735
|
Resubstitution Estimate
|
863
|
N
|
|
|
|
|
Model Evaluation
|
(Sammut & Webb, 2011) ⇒ . (2011). “Resubstitution Estimate.” In: (Sammut & Webb, 2011)
|
|
| 736
|
Reward
|
863
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Reward.” In: (Sammut & Webb, 2011)
|
|
| 742
|
ROC Convex Hull
|
875
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “ROC Convex Hull.” In: (Sammut & Webb, 2011)
|
|
| 743
|
ROC Curve
|
875
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “ROC Curve.” In: (Sammut & Webb, 2011)
|
|
| 744
|
Rotation Forests
|
875
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Rotation Forests.” In: (Sammut & Webb, 2011)
|
|
| 749
|
Samuel's Checkers Player
|
881
|
N
|
|
|
|
|
Machine Learning and Game Playing
|
(Sammut & Webb, 2011) ⇒ . (2011). “Samuel's Checkers Player.” In: (Sammut & Webb, 2011)
|
|
| 760
|
Sensitivity
|
901
|
N
|
|
|
|
Recall ; True positive rate
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Sensitivity.” In: (Sammut & Webb, 2011)
|
|
| 763
|
Sequential Data
|
902
|
N
|
|
|
|
Sequence data
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Sequential Data.” In: (Sammut & Webb, 2011)
|
|
| 767
|
Shannon's Information
|
902
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Shannon's Information.” In: (Sammut & Webb, 2011)
|
|
| 768
|
Shattering Coefficient
|
902
|
N
|
|
|
|
Growth function
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Shattering Coefficient.” In: (Sammut & Webb, 2011)
|
|
| 771
|
Simple Recurrent Network
|
906
|
N
|
|
|
|
Elman network ; Feedforward recurrent network
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Simple Recurrent Network.” In: (Sammut & Webb, 2011)
|
|
| 773
|
Solution Concept
|
906
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Solution Concept.” In: (Sammut & Webb, 2011)
|
|
| 778
|
Specialization
|
907
|
N
|
|
|
|
|
Generalization ; Induction ; Learning as Search ; Subsumption ; Logic of Generality
|
(Sammut & Webb, 2011) ⇒ . (2011). “Specialization.” In: (Sammut & Webb, 2011)
|
|
| 779
|
Specificity
|
907
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Specificity.” In: (Sammut & Webb, 2011)
|
|
| 780
|
Spectral Clustering
|
907
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Spectral Clustering.” In: (Sammut & Webb, 2011)
|
|
| 782
|
Speedup Learning for Planning
|
911
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Speedup Learning for Planning.” In: (Sammut & Webb, 2011)
|
|
| 783
|
Spike-Timing-Dependent Plasticity
|
912
|
N
|
|
|
|
|
Biological Learning ; Synaptic Plasticity ; Hebb Rule ; Spike Timing Dependent Plasticity
|
(Sammut & Webb, 2011) ⇒ . (2011). “Spike-Timing-Dependent Plasticity.” In: (Sammut & Webb, 2011)
|
|
| 784
|
Sponsored Search
|
912
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Sponsored Search.” In: (Sammut & Webb, 2011)
|
|
| 785
|
Squared Error
|
912
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Squared Error.” In: (Sammut & Webb, 2011)
|
|
| 786
|
Squared Error Loss
|
912
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Squared Error Loss.” In: (Sammut & Webb, 2011)
|
|
| 787
|
Stacked Generalization
|
912
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Stacked Generalization.” In: (Sammut & Webb, 2011)
|
|
| 790
|
State
|
912
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “State.” In: (Sammut & Webb, 2011)
|
|
| 796
|
Stratified Cross Validation
|
928
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Stratified Cross Validation.” In: (Sammut & Webb, 2011)
|
|
| 797
|
Stream Mining
|
928
|
N
|
|
|
|
|
Clustering Data Stream ; Online Learning
|
(Sammut & Webb, 2011) ⇒ . (2011). “Stream Mining.” In: (Sammut & Webb, 2011)
|
|
| 798
|
String Kernel
|
929
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “String Kernel.” In: (Sammut & Webb, 2011)
|
|
| 799
|
String Matching Algorithm
|
929
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “String Matching Algorithm.” In: (Sammut & Webb, 2011)
|
|
| 805
|
Subgroup Discovery
|
933
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Subgroup Discovery.” In: (Sammut & Webb, 2011)
|
|
| 811
|
Supervised Learning
|
941
|
N
|
|
|
|
|
Reinforcement Learning ; Unsupervised Learning
|
(Sammut & Webb, 2011) ⇒ . (2011). “Supervised Learning.” In: (Sammut & Webb, 2011)
|
|
| 813
|
Swarm Intelligence
|
946
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Swarm Intelligence.” In: (Sammut & Webb, 2011)
|
|
| 816
|
Symmetrization Lemma
|
954
|
N
|
|
|
|
Basic Lemma
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Symmetrization Lemma.” In: (Sammut & Webb, 2011)
|
|
| 822
|
TD-Gammon
|
955
|
N
|
|
|
|
|
Machine Learning and Game Playing
|
(Sammut & Webb, 2011) ⇒ . (2011). “TD-Gammon.” In: (Sammut & Webb, 2011)
|
|
| 827
|
Test Data
|
962
|
N
|
|
|
|
Evaluation data ; Test instances
|
Test set
|
(Sammut & Webb, 2011) ⇒ . (2011). “Test Data.” In: (Sammut & Webb, 2011)
|
|
| 829
|
Test Set
|
962
|
N
|
|
|
|
Evaluation data ; Evaluation set ; Test data
|
Data Set
|
(Sammut & Webb, 2011) ⇒ . (2011). “Test Set.” In: (Sammut & Webb, 2011)
|
|
| 830
|
Test Time
|
962
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Test Time.” In: (Sammut & Webb, 2011)
|
|
| 831
|
Test-Based Coevolution
|
962
|
N
|
|
|
|
Competitive Coevolution
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Test-Based Coevolution.” In: (Sammut & Webb, 2011)
|
|
| 841
|
TF-IDF
|
986
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “TF-IDF.” In: (Sammut & Webb, 2011)
|
|
| 849
|
Training Data
|
989
|
N
|
|
|
|
Training examples ; Training instances ; Training set
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Training Data.” In: (Sammut & Webb, 2011)
|
|
| 852
|
Training Set
|
990
|
N
|
|
|
|
Training Data
|
Data Set ; Training Data
|
(Sammut & Webb, 2011) ⇒ . (2011). “Training Set.” In: (Sammut & Webb, 2011)
|
|
| 853
|
Training Time
|
990
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Training Time.” In: (Sammut & Webb, 2011)
|
|
| 857
|
Transition Probabilities
|
990
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Transition Probabilities.” In: (Sammut & Webb, 2011)
|
|
| 861
|
True Negative
|
999
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “True Negative.” In: (Sammut & Webb, 2011)
|
|
| 863
|
True Positive
|
999
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “True Positive.” In: (Sammut & Webb, 2011)
|
|
| 868
|
Underlying Objective
|
1001
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Underlying Objective.” In: (Sammut & Webb, 2011)
|
|
| 873
|
Unlabeled Data
|
1008
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Unlabeled Data.” In: (Sammut & Webb, 2011)
|
|
| 875
|
Unstable Learner
|
1008
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Unstable Learner.” In: (Sammut & Webb, 2011)
|
|
| 876
|
Unsupervised Learner
|
1009
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Unsupervised Learner.” In: (Sammut & Webb, 2011)
|
|
| 887
|
Viterbi Algorithm
|
1025
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Viterbi Algorithm.” In: (Sammut & Webb, 2011)
|
|
| 892
|
Word Sense Discrimination
|
1030
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Word Sense Discrimination.” In: (Sammut & Webb, 2011)
|
|
| 894
|
Zero-One Loss
|
1031
|
N
|
|
|
|
|
|
(Sammut & Webb, 2011) ⇒ . (2011). “Zero-One Loss.” In: (Sammut & Webb, 2011)
|
|