ID

Term

Page

Type

Redirect

Author(s)

mult alp

Synonym

Cross References

GMRKB Entry


2

Absolute Error Loss

9

S

Mean Absolute Error







4

ACO

10

S

Ant Colony Optimization







9

Adaptive Control Processes

20

S

Bayesian Reinforcement Learning







12

Adaptive Systems

36

S

Complexity in Adaptive Systems







14

AgentBased Computational Models

36

S

Artificial Societies







15

AgentBased Modeling and Simulation

36

S

Artificial Societies







17

AIS

36

S

Artificial Immune Systems







19

Analogical Reasoning

37

S

InstanceBased Learning







20

Analysis of Text

37

S

Text Mining







21

Analytical Learning

37

S

Deductive Learning ; ExplanationBased Learning







24

AODE

40

S

Average OneDependence Estimators







25

Apprenticeship Learning

40

S

Behavioural Cloning







26

Approximate Dynamic Programming

40

S

Value Function Approximation







29

AQ

41

S

Rule Learning







30

ARL

41

S

AverageReward Reinforcement Learning







31

ART

41

S

Adaptive RealTime Dynamic Programming







32

ARTDP

41

S

Adaptive RealTime Dynamic Programming







39

Associative Bandit Problem

50

S

Associative Reinforcement Learning







42

Attribute Selection

54

S

Feature Selection







44

AUC

54

S

Area Under Curve







46

AverageCost NeuroDynamic Programming

63

S

AverageReward Reinforcement Learning







47

AverageCost Optimization

63

S

AverageReward Reinforcement Learning







49

AveragePayoff Reinforcement Learning

64

S

AverageReward Reinforcement Learning







52

Backprop

69

S

Backpropagation







56

BanditProblem with Side Information

73

S

Associative Reinforcement Learning







57

Bandit Problem with Side Information

73

S

Associative Reinforcement Learning







58

Basic Lemma

73

S

Symmetrization Lemma







62

Bayes Adaptive Markov Decision Processes

74

S

Bayesian Reinforcement Learning







63

Bayes Net

74

S

Bayesian Network







66

Bayesian Model Averaging

81

S

Learning Graphical Models







72

Belief State Markov Decision Processes

97

S

Partially Observable Markov Decision Processes







78

BiasVariance Tradeoffs

110

S

BiasVariance







81

Binning

111

S

Discretization







89

Bounded Differences Inequality

137

S

McDiarmid's Inequality







90

BP

137

S

Backpropagation







93

C4.5

139

S

Decision Tree







97

CART

147

S

Decision Tree







98

Cascor

147

S

CascadeCorrelation







99

Case

147

S

Instance







100

CaseBased Learning

147

S

InstanceBased Learning







104

Categorization

159

S

Classification ; Concept Learning







105

Category

159

S

Class







106

Casual Discovery

159

S

learning Graphical Models







108

CBR

166

S

CaseBased Reasoning







109

CC

166

S

CascadeCorrelation







110

Certainty Equivalence Principle

166

S

Internal Model Control







111

Characteristic

166

S

Attribute







112

City Block Distance

166

S

Manhattan Distance







116

Classification Learning

171

S

Concept Learning







117

Classification Tree

171

S

Decision Tree







123

Closest Point

179

S

Nearest Neighbor







131

Clustering of Nonnumerical Data

183

S

Categorical Data Clustering







132

Clustering with Advice

183

S

Correlation Clustering







133

Clustering with Constraints

183

S

Correlation Clustering







134

Clustering with Qualitative Information

183

S

Correlation Clustering







135

Clustering with Side Information

183

S

Correlation Clustering







136

CN2

183

S

Rule Learning







137

CoTraining

183

S

SemiSupervised Learning







138

Coevolution

183

S

Coevolutionary Learning







139

Coevolutionary Computation

184

S

Coevolutionary Learning







142

Collection

189

S

Class







144

Commercial Email Filtering

193

S

Text Mining for Spam Filtering







145

Committee Machines

193

S

Ensemble Learning







146

Community Detection

193

S

Group Detection







148

Competitive Coevolution

194

S

TestBased Coevolution







150

Complex Adaptive System

194

S

Complexity Adaptive Systems







155

Computational Discovery of Quantitative Laws

202

S

Equation Discovery







162

Connection Strength

210

S

Weight







164

Connectivity

219

S

Topology of a Neural Network







169

Content Match

226

S

Text Mining for Advertising







171

ContentBased Recommending

226

S

ContentBased Filtering







172

ContextSensitive Learning

226

S

Concept Drift







173

Contextual Advertising

226

S

Text Mining for Advertising







174

Continual Learning

226

S

LifeLong Learning







177

Cooperative Coevolution

226

S

Compositional Coevolution







178

CoReference Resolution

226

S

Entity Resolution







185

CosttoGo Function Approximation

235

S

Value Function Approximation







187

Covering Algorithm

238

S

Rule Learning







197

Data Mining On Text

259

S

Text Mining







199

Data Processing

260

S

Data Preparation







209

Decision Trees for Regression

267

S

Regression Trees







211

Deduplication

267

S

Entity Resolution







213

Deep Belief Networks

269

S

Deep Belief Nets







216

Dependency Directed Backtracking

274

S

Intelligent backtracking







218

Deterministic Decision Rule

274

S

Decision Rule







221

Dimensionality Reduction on Text via Feature Selection

279

S

Feature Selection in Text Mining







222

Directed Graphs

279

S

Digraphs







228

Distance

289

S

Similarity Measures







229

Distance Functions

289

S

Similarity Measures







230

Distance Measures

289

S

Similarity Measures







231

Distance Metrics

289

S

Similarity Measures







232

DistributionFree Learning

289

S

PAC Learning







236

Dual Control

298

S

Bayesian Reinforcement Learning ; Partially Observable Markov Decision Process







237

Duplicate Detection

298

S

Entity Resolution







238

Dynamic Bayesian Network

298

S

Learning Graphical Models







239

Dynamic Decision Network

298

S

Partially Observable Markov Decision Processes







242

Dynamic Programming for Relational Domains

308

S

Symbolic Dynamic Programming







245

EBL

309

S

ExplanationBased Learning







246

Echo State Network

309

S

Reservoir Computing







247

ECOC

309

S

Error Correcting Output Codes







248

Edge Prediction

309

S

Link Prediction







250

EFSC

311

S

Evolutionary Feature Selection and Construction







251

Elman Network

311

S

Simple Recurrent Network







252

EM Algorithm

311

S

Expectation Maximization Clustering







253

Embodied Evolutionary Learning

311

S

Evolutionary Robotics







259

EP

326

S

Expectation Propagation







263

Error

330

S

Error Rate







264

Error Correcting Output

331

S

ECOC







265

Error Curve

331

S

Learning Curves in Machine Learning







268

Estimation of Density Level Sets

331

S

DensityBased Clustering







270

Evaluation Data

332

S

Test Data ; Test Set







271

Evaluation Set

332

S

Test Set







272

Evolution of Agent Behaviors

332

S

Evolutionary Robotics







273

Evolution of Robot Control

332

S

Evolutionary Robotics







280

Evolutionary Computing

353

S

Evolutionary Algorithms







281

Evolutionary Constructive Induction

353

S

Evolutionary Feature Selection and Construction







282

Evolutionary Feature Selection

353

S

Evolutionary Feature Selection and Construction







284

Evolutionary Feature Synthesis

357

S

Evolutionary Feature Selection and Construction







287

Evolutionary Grouping

369

S

Evolutionary Clustering







290

Evolving Neural Networks

382

S

Neuroevolution







291

Example

382

S

Instance







292

ExampleBased Learning

382

S

Inductive Programming







293

Expectation Maximization Algorithm

382

S

ExpectationMaximization Algorithm







297

Experience Curve

387

S

Learning Curves in Machine Learning







298

ExperienceBased Reasoning

388

S

CaseBased Reasoning







300

ExplanationBased Generalization for Planning

388

S

ExplanationBased Learning for Planning







306

Feature

397

S

Attribute







307

Feature Construction

397

S

Data Preparation







309

Feature Extraction

401

S

Dimensionality Reduction







310

Feature Reduction

402

S

Feature Selection







313

Feature subset

410

S

Feature Selection







314

Feedforward Recurrent Network

410

S

Simple Recurrent Network







315

Finite Mixture Model

410

S

Mixture Model







317

FirstOrder Predicate Calculus

415

S

FirstOrder Logic







318

FirstOrder Predicate Logic

415

S

FirstOrder Logic







320

FMeasure

416

S

Precision and Recall







321

Foil

415

S

Rule Learning







325

Frequent Set

423

S

Frequent Itemset







326

Functional Trees

423

S

Model trees







333

Generality And Logic

447

S

Logic of Generality







336

Generalization Performance

454

S

Algorithm Evaluation







337

Generalized Delta Rule

454

S

Backpropagation







338

GeneraltoSpecific Search

454

S

Learning as Search







342

Genetic Attribute Construction

457

S

Evolutionary Feature Selection and Construction







343

Genetic Clustering

457

S

Evolutionary Clustering







344

Genetic Feature Selection

457

S

Evolutionary Feature Selection and Construction







345

Genetic Grouping

457

S

Evolutionary Clustering







346

Genetic Neural Networks

457

S

Neuroevolution







348

GeneticsBased Machine Learning

457

S

Classifier System







351

Gram Matrix

458

S

Kernel Matrix







352

Grammar Learning

458

S

Grammatical Interface







354

Grammatical Tagging

459

S

POS Tagging







363

Grouping

492

S

Categorical Data Clustering







365

Growth Function

492

S

Shattering Coefficient







369

Heuristic Rewards

493

S

Reward Shaping







372

HighDimensional Clustering

502

S

Document Clustering







374

HMM

506

S

Hidden Markov Models







375

HoldOneOut Error

506

S

LeaveOneOutError







376

Holdout Data

506

S

Holdout Set







383

ID3

515

S

Decision Tree







384

Identification

515

S

Classification







385

Identity Uncertainty

515

S

Entity Resolution







386

Idiot's Bayes

515

S

Naïve Bayes







387

Immune Computing

515

S

Artificial Immune Systems







389

ImmuneInspired Computing

515

S

Artificial Immune Systems







390

Immunocomputing

515

S

Artificial Immune Systems







391

Immunological Computation

515

S

Artificial Immune Systems







392

Implication

515

S

Entailment







393

Improvement Curve

515

S

Learning Curves in Machine Learning







397

Induction as Inverted Deduction

522

S

Logic of Generality







402

Inductive Inference Rules

528

S

Logic of Generality







406

Inductive Program Synthesis

537

S

Inductive Programming







410

Inequalities

548

S

Generalization Bounds







412

Information Theory

548

S

Minimum Description Length Principle ; Minimum Message Length







415

Instance Language

549

S

Observation Language







420

Intent Reinforcement Learning

553

S

Inverse Reinforcement Learning







424

Inverse Optical Control

554

S

Inverse Reinforcement Learning







427

Is More General Than

558

S

Logic of Generality







428

Is More Specific Than

558

S

Logic of Generality







429

Item

558

S

Instance







430

Iterative Classification

558

S

Collective Classification







432

Junk Email Filtering

559

S

Text Mining for Spam Filtering







438

Kernel Density Estimation

566

S

Density Estimation







441

Kernel Shaping

570

S

Long Distance Metric Adaptation ; Locally Weighted Regression for Control







442

KernelBased Reinforcement Learning

570

S

InstanceBased Reinforcement Learning







443

Kernels

570

S

Gaussian Process







444

Kind

570

S

Class







445

Knowledge Discovery

570

S

Text Mining for Semantic Web







446

Kohonen Maps

570

S

SelfOrganizing Maps







448

L1Distance

571

S

Manhattan Distance







452

Laplace Estimate

571

S

Rule Learning







453

Latent Class Model

571

S

Mixture Model







457

Learning Bayesian Networks

577

S

Learning Graphical Models







458

Learning Bias

577

S

Inductive Bias







459

Learning By Demonstration

577

S

Behavioral Cloning







460

Learning Classifier Systems

577

S

Classifier Systems







461

Learning Control Rules

577

S

Behavioral Cloning







463

Learning from Complex Data

580

S

Learning from Structured Data







464

Learning from Labeled and Unlabeled Dated

580

S

SemiSupervised Learning







465

Learning from Nonpropositional Data

580

S

Learning from Structured Data







466

Learning from Preferences

580

S

Preference Learning







469

Learning in Logic

590

S

Inductive Logic Programming







470

Learning in Worlds with Objects

590

S

Relational Reinforcement Learning







473

Learning with Different Classification Costs

595

S

CostSensitive Learning







474

Learning with Hidden Context

595

S

Concept Drift







475

Learning Word Senses

595

S

Word Sense Disambiguation







476

LeastSquares Reinforcement Learning Methods

595

S

Curse of Dimensionality ; Feature Selection ; Radial Basis Functions ; Reinforcement Learning ; Temporal Difference Learning ; Value Function Approximation







478

LessonsLearned Systems

601

S

CaseBase Reasoning







479

Lifelong Learning

601

S

Cumulative Learning







480

LifeLong Learning

601

S

Continual Learning







484

Linear Regression Tree

606

S

Model Trees







486

Link Analysis

606

S

Link Mining and Link Discovery







489

LinkBased Classification

613

S

Collective Classification







490

Liquid State Machine

613

S

Reservoir Computing







492

Local Feature Selection

613

S

Projective Clustering







497

Logical Consequence

631

S

Entailment







498

Logical Regression Tree

631

S

FirstOrder Regression Tree







500

Logit Model

631

S

Logistics Regression







503

LOO Error

632

S

LeaveOneOut Error







507

LWPR

632

S

Locally Weighted Regression for Control







508

LWR

632

S

Locally Weighted Regression for Control







510

mEstimate

633

S

Rule Learning







515

Market Basket Analysis

639

S

Basket Analysis







516

Markov Blanket

639

S

Graphical Models







517

Markov Chain

639

S

Markov Process







520

Markov Model

646

S

Markov Process







521

Markov Net

646

S

Markov Network







524

Markov Random Field

647

S

Markov Network







529

MCMC

652

S

Markov Chain Monte Carlo







530

MDL

652

S

Minimum Description Length Principle







532

Mean Error

652

S

Mean Absolute Error







537

Memory Organization Packets

661

S

Dynamic Memory Model







538

MemoryBased

661

S

InstanceBased Learning







539

MemoryBased Learning

661

S

CaseBased Reasoning







540

MergePurge

661

S

Entity Resolution







547

Minimum Encoding Inference

668

S

Minimum Description Length Principle ; Minimum Message Length







550

Missing Values

680

S

Missing Attribute Values







551

MistakeBounded Learning

680

S

Online Learning







552

Mixture Distribution

680

S

Mixture Model







554

Mixture Model

683

S

Mixture Model







555

Mode Analysis

683

S

DensityBased Clustering







558

Model Space

683

S

Hypothesis Space







561

ModelBased Control

689

S

Internal Model Control







563

Modularity Detection

693

S

Group Detection







564

MOO

693

S

Multi Objective Optimization







565

Morphosyntactic Disambiguation

693

S

POS Tagging







567

Most Similar Point

694

S

Nearest Neighbor







571

MultiArmed Bandit

699

S

kArmed Bandit







572

MultiArmed Bandit Problem

699

S

kArmed Bandit







574

MultiCriteria Optimization

701

S

MultiObjective Optimization







577

Multiple Classifier Systems

711

S

Ensemble Learning







584

NCLearning

714

S

Negative Correlation Learning







585

NCL

714

S

Negative Correlation Learning







587

Nearest Neighbor Methods

715

S

InstanceBased Learning







590

Network Analysis

716

S

LinkMining and Link Discovery







591

Network Clustering

716

S

Graph Clustering







592

Networks with Kernel Functions

716

S

Radial Basis Function Networks







594

Neural Network Architecture

716

S

Topology of a Neural Network







595

NeuroDynamic Programming

716

S

Value Function Approximation







598

Node

721

S

Neuron







603

Nonparametric Bayesian

722

S

Gaussian Process







604

Nonparametric Cluster Analysis

722

S

DensityBased Clustering







605

NonParametric Methods

722

S

InstanceBased Learning







607

Nonstationary Kernels

731

S

Local Distance Metric Adaptation







608

Nonstationary Kernels Supersmoothing

731

S

Locally Weighted Regression for Control







609

Normal Distribution

731

S

Gaussian Distribution







613

Object

733

S

Instance







614

Object Consolidation

733

S

Entity Resolution







615

Object Space

733

S

Example Space







618

Ockham's Razor

736

S

Occam's Razor







619

Offline Learning

736

S

Batch Learning







620

OneStep Reinforcement Learning

736

S

Associative Reinforcement Learning







631

Overtraining

744

S

Overfitting







633

PAC Identification

745

S

PAC Learning







642

Perception

773

S

Online Learning







643

Piecewise Constant Models

773

S

Regression Trees







644

Piecewise Linear Models

773

S

Model Trees







645

Plan Recognition

774

S

Inverse Reinforcement Learning







647

Policy Search

776

S

Markov Decision Processes ; Policy Gradient Methods







648

POMDPs

776

S

Partially Observable Markov Decision Processes







650

Positive Definite

779

S

Positive Semidefinite







651

Positive Predictive Value

779

S

Precision







653

Posterior

780

S

Posterior Probability







660

Predicate Calculus

781

S

FirstOrder Logic







662

Predicate Logic

782

S

FirstOrder Logic







663

Prior Probabilities

782

S

Bayesian Nonparametric Models







665

Predication with Expert Advice

782

S

Online Learning







666

Predictive Software Model

782

S

Predictive Techniques in Software Engineering







672

Prior

795

S

Prior Probability







673

Privacy Preserving Data Mining

795

S

PrivacyRelated Aspects and Techniques







676

Probably Approximately Correct Learning

805

S

PAC Learning







678

Program Synthesis From Examples

805

S

Inductive Programming







680

Programming by Example

805

S

Programming by Demonstration







681

Programming from Traces

806

S

TraceBased Programming







684

Property

812

S

Attribute







691

Quadratic Loss

819

S

Mean Squared Error







692

Qualitative Attribute

819

S

Categorical Attribute







694

Quantitative Attribute

820

S

Numeric Attribute







696

Rademacher Average

823

S

Rademacher Complexity







698

Radial Basis Function Approximation

823

S

Radial Basis Function Networks







700

Radial Basis Function Neural Networks

827

S

Radial Basis Function Networks







701

Random Decision Forests

827

S

Random Forests







704

Random Subspaces

828

S

Random Subspace Method







705

Randomized Decision Rule

828

S

Markovian Decision Rule







710

Receiver Operating Characteristic Analysis

829

S

ROC Analysis







711

Recognition

829

S

Classification







713

Record Linkage

838

S

Entity Resolution







714

Recurrent Associative Memory

838

S

Hopfield Network







715

Recursive Partitioning

838

S

DivideandConquer Learning







716

Reference Reconciliation

838

S

Entity Resolution







720

Regularization Networks

849

S

Radial Basis Function Networks







722

Reinforcement Learning in Structured Domains

851

S

Relational Reinforcement Learning







724

Relational Data Mining

851

S

Inductive LogicProgramming







725

Relational Dynamic Programming

851

S

Symbolic Dynamic Programming







727

Relational Regression Learning

857

S

FirstOrder Regression Tree







729

Relational Value Iteration

862

S

Symbolic Dynamic Programming







730

Relationship Extraction

862

S

Link Prediction







732

Representation Language

863

S

Hypothesis Language







734

Resolution

863

S

FirstOrder Logic







737

Reward Selection

863

S

Reward Shaping







739

RIPPER

865

S

Rule Learning







745

RSM

875

S

Random Subspace Method







748

Sample Complexity

881

S

Generalization Bounds







750

Saturation

881

S

Bottom Clause







751

SDP

881

S

Symbolic Dynamic Programming







752

Search Bias

881

S

Learning as Search







754

SelfOrganizing Feature Maps

886

S

SelfOrganizing Maps







756

Semantic Mapping

888

S

Text Visualization







762

Sequence Data

902

S

Sequential Data







764

Sequential Inductive Transfer

902

S

Cumulative Learning







765

Sequential Prediction

902

S

Online Learning







766

Set

902

S

Class







770

Simple Bayes

906

S

Naïve Bayes







772

SMT

906

S

Statistical Machine Translation







774

Solving Semantic Ambiguity

906

S

Word Sense Disambiguation







775

SOM

906

S

SelfOrganizing Maps







776

SORT

906

S

Class







777

Spam Detection

906

S

Text Mining for Spam Filtering







788

Stacking

912

S

Stacked Generalization







789

Starting Clause

912

S

Bottom Clause







791

Statistical Learning

912

S

Inductive Learning







793

Statistical Natural Language Processing

916

S

Maximum Entropy Models for Natural Language Processing







800

Structural Credit Assignment

929

S

Credit Assignment







802

Structure

930

S

Topology of a Neural Network







803

Structured Data Clustering

930

S

Graph Clustering







807

Subspace Clustering

937

S

Projective Clustering







809

Supersmoothing

938

S

Local Distance Metric Adaptation







815

Symbolic Regression

954

S

Equation Discovery







817

Synaptic E. Cacy

954

S

Weight







819

Tagging

955

S

POS Tagging







820

TAN

955

S

True Augmented Naïve Bayes







821

Taxicab Norm Distance

955

S

Manhattan Distance







823

TDIDT Strategy

956

S

DivideandConquer Learning







824

Temporal Credit Assignment

956

S

Credit Assignment







825

Temporal Data

956

S

Time Series







828

Test Instances

962

S

Test Data







832

Text Clustering

962

S

Document Clustering







833

Text Learning

962

S

Text Mining







839

Text Spatialization

980

S

Text Visualization







842

Threshold Phenomena in Learning

987

S

Phase Transitions in Machine Learning







843

Time Sequence

987

S

Time Series







845

Topic Mapping

988

S

Text Visualization







848

Training Curve

989

S

Learning Curves in Machine Learning







850

Training Examples

989

S

Training Data







851

Training Instances

990

S

Training Data







854

Trait

990

S

Attribute







855

Trajectory Data

990

S

SemiSupervised Learning ; SemiSupervised Text Processing







856

Transfer of Knowledge Across Domains

990

S

Inductive Transfer







860

TreeBased Regression

999

S

Regression Trees







862

True Negative Rule

999

S

Specificity







864

True Positive Rate

999

S

Sensitivity







865

Type

999

S

Class







866

Typical Complexity of Learning

999

S

Phase Transitions in Machine Learning







869

Unit

1001

S

Neuron







871

Unknown Attribute Values

1008

S

Missing Attribute Values







872

Unknown Values

1008

S

Missing Attribute Values







874

Unsolicited Commercial Email

1008

S

Text Mining for Spam Filtering







877

Unsupervised Learner on Document Datasets

1009

S

Document Clustering







878

Utility Problem

1009

S

ExplanationBased Learning







880

Variable Selection

1021

S

Feature Selection







881

Variable Subset Selection

1021

S

Feature Selection







882

Variance

1021

S

Bias Variance Decomposition







883

Variance Hint

1021

S

Variance Bias







885

Vector Optimization

1024

S

MultiObjective Optimization







888

Web Advertising

1027

S

Text Mining for Advertising







890

WithinSample Evaluation

1027

S

InSample Evaluation






