Encyclopedia of Machine Learning


Encyclopedia of Machine Learning (2011) .. edited by C.Sammut & G.I.Webb


0-9

1-Norm Distance. 1

A

Antonis C. Kakas:
Abduction. 3-9

Absolute Error Loss. 9

Accuracy. 9-10

ACO. 10

Actions. 10

David Cohn:
Active Learning. 10-14

Sanjoy Dasgupta:
Active Learning Theory. 14-19

Adaboost. 19

Adaptive Control Processes. 19

Andrew G. Barto:
Adaptive Real-Time Dynamic Programming. 19-22

Gail A. Carpenter, Stephen Grossberg:
Adaptive Resonance Theory. 22-35

Adaptive System. 35

Agent. 35

Agent-Based Computational Models. 35

Agent-Based Modeling and Simulation. 35

Agent-Based Simulation Models. 35

AIS. 35

Geoffrey I. Webb:
Algorithm Evaluation. 35-36

Analogical Reasoning. 36

Analysis of Text. 36

Analytical Learning. 36

Marco Dorigo, Mauro Birattari:
Ant Colony Optimization. 36-39

Anytime Algorithm. 39

AODE. 39

Apprenticeship Learning. 39

Approximate Dynamic Programming. 39

Hannu Toivonen:
Apriori Algorithm. 39-40

AQ. 40

Area Under Curve. 40

ARL. 40

ART. 40

ARTDP. 40

Jon Timmis:
Artificial Immune Systems. 40-44

Artificial Life. 44

Artificial Neural Networks. 44

Jürgen Branke:
Artificial Societies. 44-48

Assertion. 48

Hannu Toivonen:
Association Rule. 48-49

Associative Bandit Problem. 49

Alexander L. Strehl:
Associative Reinforcement Learning. 49-51

Chris Drummond:
Attribute. 51-53

Attribute Selection. 53

Attribute-Value Learning. 53

AUC. 53

Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Autonomous Helicopter Flight Using Reinforcement Learning. 53-61

Average-Cost Neuro-Dynamic Programming. 63

Average-Cost Optimization. 63

Fei Zheng, Geoffrey I. Webb:
Averaged One-Dependence Estimators. 63-64

Average-Payoff Reinforcement Learning. 64

Prasad Tadepalli:
Average-Reward Reinforcement Learning. 64-68

B

Backprop. 69-73

Paul W. Munro:
Backpropagation. 73

Bagging. 73

Bake-Off. 73

Bandit Problem with Side Information. 73

Bandit Problem with Side Observations. 73

Basic Lemma. 73

Hannu Toivonen:
Basket Analysis. 74

Batch Learning. 74

Baum-Welch Algorithm. 74

Bayes Adaptive Markov Decision Processes. 74

Bayes Net. 74

Geoffrey I. Webb:
Bayes Rule. 74-75

Wray L. Buntine:
Bayesian Methods. 75-81

Bayesian Model Averaging. 81

Bayesian Network. 81

Peter Orbanz, Yee Whye Teh:
Bayesian Nonparametric Models. 81-89

Pascal Poupart:
Bayesian Reinforcement Learning. 90-93

Claude Sammut:
Beam Search. 93

Claude Sammut:
Behavioral Cloning. 93-97

Belief State Markov Decision Processes. 97

Bellman Equation. 97

Bias. 97

Hendrik Blockeel:
Bias Specification Language. 98-100

Bias Variance Decomposition. 100-101

Dev G. Rajnarayan, David Wolpert:
Bias-Variance Trade-offs: Novel Applications. 101-110

Bias-Variance Trade-offs. 110

Bias-Variance-Covariance Decomposition. 111

Bilingual Lexicon Extraction. 111

Binning. 111

Wulfram Gerstner:
Biological Learning: Synaptic Plasticity, Hebb Rule and Spike TimingDependent Plasticity. 111-132

C. David Page Jr., Sriraam Natarajan:
Biomedical Informatics. 132

Blog Mining. 132

Geoffrey E. Hinton:
Boltzmann Machines. 132-136

Boosting. 136-137

Bootstrap Sampling. 137

Bottom Clause. 137

Bounded Differences Inequality. 137

BP. 137

Breakeven Point. 137-138

C

C4.5. 139

Candidate-Elimination Algorithm. 139

Cannot-Link Constraint. 139

CART. 147

Thomas R. Shultz, Scott E. Fahlman:
Cascade-Correlation. 139-147

Cascor. 147

Case. 147

Case-Based Learning. 147

Susan Craw:
Case-Based Reasoning. 147-154

Categorical Attribute. 154

Periklis Andritsos, Panayiotis Tsaparas:
Categorical Data Clustering. 154-159

Categorization. 159

Category. 159

Causal Discovery. 159

Ricardo Silva:
Causality. 159-166

CBR. 166

CC. 166

Certainty Equivalence Principle. 166

Characteristic. 166

City Block Distance. 166

Chris Drummond:
Class. 166-171

Charles X. Ling, Victor S. Sheng:
Class Imbalance Problem. 171

Chris Drummond:
Classification. 171

Classification Algorithms. 171

Classification Learning. 171

Classification Tree. 171

Pier Luca Lanzi:
Classifier Systems. 172-178

Clause. 178-179

Clause Learning. 179

Click-Through Rate (CTR). 179

Clonal Selection. 179

Closest Point. 179

Cluster Editing. 179

Cluster Ensembles. 179

Cluster Optimization. 179

Clustering. 180

Clustering Aggregation. 180

Clustering Ensembles. 180

João Gama:
Clustering from Data Streams. 180-183

Clustering of Nonnumerical Data. 183

Clustering with Advice. 183

Clustering with Constraints. 183

Clustering with Qualitative Information. 183

Clustering with Side Information. 183

CN2. 183

Co-Reference Resolution. 226

Co-Training. 183

Coevolution. 183

Coevolutionary Computation. 184

R. Paul Wiegand:
Coevolutionary Learning. 184-189

Collaborative Filtering. 189

Collection. 189

Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor:
Collective Classification. 189-193

Commercial Email Filtering. 193

Committee Machines. 193

Community Detection. 193

Comparable Corpus. 194

Competitive Coevolution. 194

Competitive Learning. 194

Complex Adaptive System. 194

Jun He:
Complexity in Adaptive Systems. 194-198

Sanjay Jain, Frank Stephan:
Complexity of Inductive Inference. 198-201

Compositional Coevolution. 201

Sanjay Jain, Frank Stephan:
Computational Complexity of Learning. 201-202

Computational Discovery of Quantitative Laws. 202

Claude Sammut, Michael Bonnell Harries:
Concept Drift. 202-205

Claude Sammut:
Concept Learning. 205-208

Conditional Random Field. 208

Confirmation Theory. 209

Kai Ming Ting:
Confusion Matrix. 209

Bernhard Pfahringer:
Conjunctive Normal Form. 209-210

Connection Strength. 210

John Case, Sanjay Jain:
Connections Between Inductive Inference and Machine Learning. 210-219

Connectivity. 219

Consensus Clustering. 219-220

Kiri L. Wagstaff:
Constrained Clustering. 220-221

Siegfried Nijssen:
Constraint-Based Mining. 221-225

Constructive Induction. 225

Content Match. 226

Content-Based Filtering. 226

Content-Based Recommending. 226

Context-Sensitive Learning. 226

Contextual Advertising. 226

Continual Learning. 226

Continuous Attribute. 226

Contrast Set Mining. 226

Cooperative Coevolution. 226

Anthony Wirth:
Correlation Clustering. 227-231

Correlation-Based Learning. 231

Cost. 231

Cost Function. 231

Cost-Sensitive Classification. 231

Charles X. Ling, Victor S. Sheng:
Cost-Sensitive Learning. 231-235

Cost-to-Go Function Approximation. 235

Xinhua Zhang:
Covariance Matrix. 235-238

Covering Algorithm. 238

Claude Sammut:
Credit Assignment. 238-242

Cross-Language Document Categorization. 242

Cross-Language Information Retrieval. 242

Cross-Language Question Answering. 242

Nicola Cancedda, Jean-Michel Renders:
Cross-Lingual Text Mining. 243-249

Cross-Validation. 249

Pietro Michelucci, Daniel Oblinger:
Cumulative Learning. 249-257

Eamonn J. Keogh, Abdullah Mueen:
Curse of Dimensionality. 257-258

D

Data Mining On Text. 259

Geoffrey I. Webb:
Data Preparation. 259-260

Data Preprocessing. 260

Data Set. 261

DBN. 261

Decision Epoch. 261

Johannes Fürnkranz:
Decision List. 261

Johannes Fürnkranz:
Decision Lists and Decision Trees. 261-262

Decision Rule. 262

Decision Stump. 262-263

Decision Threshold. 263

Johannes Fürnkranz:
Decision Tree. 263-267

Decision Trees For Regression. 267

Deductive Learning. 267

Deduplication. 267

Geoffrey E. Hinton:
Deep Belief Nets. 267-269

Deep Belief Networks. 269

Claude Sammut:
Density Estimation. 270

Jörg Sander:
Density-Based Clustering. 270-273

Dependency Directed Backtracking. 274

Detail. 274

Deterministic Decision Rule. 274

Digraphs. 274

Michail Vlachos:
Dimensionality Reduction. 274-279

Dimensionality Reduction on Text via Feature Selection. 279

Directed Graphs. 279

Yee Whye Teh:
Dirichlet Process. 280-287

Discrete Attribute. 287

Ying Yang:
Discretization. 287-288

Discriminative Learning. 288

Disjunctive Normal Form. 289

Distance. 289

Distance Functions. 289

Distance Measures. 289

Distance Metrics. 289

Distribution-Free Learning. 289

Divide-and-Conquer Learning. 289

Dunja Mladenic, Janez Brank, Marko Grobelnik:
Document Classification. 289-293

Ying Zhao, George Karypis:
Document Clustering. 293-298

Dual Control. 298

Duplicate Detection. 298

Dynamic Bayesian Network. 298

Dynamic Decision Networks. 298

Susan Craw:
Dynamic Memory Model. 298

Martin L. Puterman, Jonathan Patrick:
Dynamic Programming. 298-308

Dynamic Programming For Relational Domains. 308

Dynamic Systems. 308

E

EBL. 309

Echo State Network. 309

ECOC. 309

Edge Prediction. 309

John Langford:
Efficient Exploration in Reinforcement Learning. 309-311

EFSC. 311

Elman Network. 311

EM Algorithm. 311

EM Clustering. 311

Embodied Evolutionary Learning. 311

Emerging Patterns. 312

Xinhua Zhang:
Empirical Risk Minimization. 312

Gavin Brown:
Ensemble Learning. 312-320

Entailment. 320-321

Indrajit Bhattacharya, Lise Getoor:
Entity Resolution. 321-326

EP. 326

Thomas Zeugmann:
Epsilon Covers. 326

Thomas Zeugmann:
Epsilon Nets. 326-327

Ljupco Todorovski:
Equation Discovery. 327-330

Error. 330

Error Correcting Output Codes. 331

Error Curve. 331

Kai Ming Ting:
Error Rate. 331

Error Squared. 331

Estimation of Density Level Sets. 331

Evaluation. 331-332

Evaluation Data. 332

Evaluation Set. 332

Evolution of Agent Behaviors. 332

Evolution of Robot Control. 332

Evolutionary Algorithms. 332

David Corne, Julia Handl, Joshua D. Knowles:
Evolutionary Clustering. 332-337

Evolutionary Computation. 337

Serafín Martínez-Jaramillo, Biliana Alexandrova-Kabadjova, Alma Lilia García-Almanza, Tonatiuh Peña Centeno:
Evolutionary Computation in Economics. 337-344

Serafín Martínez-Jaramillo, Alma Lilia García-Almanza, Biliana Alexandrova-Kabadjova, Tonatiuh Peña Centeno:
Evolutionary Computation in Finance. 344-353

Alma Lilia García-Almanza, Biliana Alexandrova-Kabadjova, Serafín Martínez-Jaramillo:
Evolutionary Computational Techniques in Marketing. 353

Evolutionary Computing. 353

Evolutionary Constructive Induction. 353

Evolutionary Feature Selection. 353

Krzysztof Krawiec:
Evolutionary Feature Selection and Construction. 353-357

Evolutionary Feature Synthesis. 357

Carlos Kavka:
Evolutionary Fuzzy Systems. 357-362

Moshe Sipper:
Evolutionary Games. 362-369

Evolutionary Grouping. 369

Christian Igel:
Evolutionary Kernel Learning. 369-373

Phil Husbands:
Evolutionary Robotics. 373-382

Evolving Neural Networks. 382

Example. 382

Example-Based Programming. 382

Expectation Maximization Algorithm. 382

Xin Jin, Jiawei Han:
Expectation Maximization Clustering. 382-383

Tom Heskes:
Expectation Propagation. 383-387

Expectation-Maximization Algorithm. 387

Experience Curve. 387

Experience-Based Reasoning. 388

Explanation. 388

Explanation-Based Generalization for Planning. 388

Gerald DeJong, Shiau Hong Lim:
Explanation-Based Learning. 388-392

Subbarao Kambhampati, Sung Wook Yoon:
Explanation-Based Learning for Planning. 392-396

F

F1-Measure. 397

F-Measure. 416

False Negative. 397

False Positive. 397

Feature. 397

Feature Construction. 397

Janez Brank, Dunja Mladenic, Marko Grobelnik:
Feature Construction in Text Mining. 397-401

Feature Extraction. 401

Feature Reduction. 402

Huan Liu:
Feature Selection. 402-406

Dunja Mladenic:
Feature Selection in Text Mining. 406-410

Feature Subset Selection. 410

Feedforward Recurrent Network. 410

Finite Mixture Model. 410

Peter A. Flach:
First-Order Logic. 410-415

First-Order Predicate Calculus. 415

First-Order Predicate Logic. 415

First-Order Regression Tree. 415-416

Foil. 416

Gemma C. Garriga:
Formal Concept Analysis. 416-418

Hannu Toivonen:
Frequent Itemset. 418

Hannu Toivonen:
Frequent Pattern. 418-422

Frequent Set. 423

Functional Trees. 423

Fuzzy Sets. 423

Fuzzy Systems. 423

G

Xinhua Zhang:
Gaussian Distribution. 425-428

Novi Quadrianto, Kristian Kersting, Zhao Xu:
Gaussian Process. 428-439

Yaakov Engel:
Gaussian Process Reinforcement Learning. 439-447

General-to-Specific Search. 454

Generality And Logic. 447

Claude Sammut:
Generalization. 447

Mark D. Reid:
Generalization Bounds. 447-454

Generalization Performance. 454

Generalized Delta Rule. 454

Bin Liu, Geoffrey I. Webb:
Generative and Discriminative Learning. 454-455

Generative Learning. 455-456

Claude Sammut:
Genetic and Evolutionary Algorithms. 456-457

Genetic Attribute Construction. 457

Genetic Clustering. 457

Genetic Feature Selection. 457

Genetic Grouping. 457

Genetic Neural Networks. 457

Moshe Sipper:
Genetic Programming. 457

Genetics-Based Machine Learning. 457

Gibbs Sampling. 457

Gini Coefficient. 457-458

Gram Matrix. 458

Grammar Learning. 458

Lorenza Saitta, Michèle Sebag:
Grammatical Inference. 458

Grammatical Tagging. 459

Charu C. Aggarwal:
Graph Clustering. 459-467

Thomas Gärtner, Tamás Horváth, Stefan Wrobel:
Graph Kernels. 467-469

Deepayan Chakrabarti:
Graph Mining. 469-471

Julian John McAuley, Tibério S. Caetano, Wray L. Buntine:
Graphical Models. 471-479

Tommy R. Jensen:
Graphs. 479-482

Claude Sammut:
Greedy Search. 482-483

Lawrence B. Holder:
Greedy Search Approach of Graph Mining. 483-489

Hossam Sharara, Lise Getoor:
Group Detection. 489-492

Grouping. 492

Growing Set. 492

Growth Function. 492

H

Hebb Rule. 493

Hebbian Learning. 493

Heuristic Rewards. 493

Antal van den Bosch:
Hidden Markov Models. 493-495

Bernhard Hengst:
Hierarchical Reinforcement Learning. 495-502

High-Dimensional Clustering. 502

John Lloyd:
Higher-Order Logic. 502-506

HMM. 506

Hold-One-Out Error. 506

Holdout Data. 506

Holdout Evaluation. 506-507

Holdout Set. 507

Risto Miikkulainen:
Hopfield Network. 507

Hendrik Blockeel:
Hypothesis Language. 507-511

Hendrik Blockeel:
Hypothesis Space. 511-513

Hypothesis Space. 513

I

ID3. 515

Identification. 515

Identity Uncertainty. 515

Idiot’s Bayes. 515

Immune Computing. 515

Immune Network. 515

Immune-Inspired Computing. 515

Immunocomputing. 515

Immunological Computation. 515

Implication. 515

Improvement Curve. 515

In-Sample Evaluation. 548

Paul E. Utgoff:
Incremental Learning. 515-518

Indirect Reinforcement Learning. 519

James Cussens:
Induction. 519-522

Induction as Inverted Deduction. 522

Inductive Bias. 522

Stefan Kramer:
Inductive Database Approach to Graphmining. 522-523

Inductive Inference. 528

Sanjay Jain, Frank Stephan:
Inductive Inference. 523-528

Inductive Inference Rules. 528

Inductive Learning. 529

Luc De Raedt:
Inductive Logic Programming. 529-537

Ljupco Todorovski:
Inductive Process Modeling. 537

Inductive Program Synthesis. 537

Pierre Flener, Ute Schmid:
Inductive Programming. 537-544

Inductive Synthesis. 544

Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares:
Inductive Transfer. 545-548

Inequalities. 548

Information Retrieval. 548

Information Theory. 548

Instance. 549

Instance Language. 549

Instance Space. 549

Eamonn J. Keogh:
Instance-Based Learning. 549-550

William D. Smart:
Instance-Based Reinforcement Learning. 550-553

Intelligent Backtracking. 553

Intent Recognition. 553

Internal Model Control. 553

Interval Scale. 553

Inverse Entailment. 553-554

Inverse Optimal Control. 554

Pieter Abbeel, Andrew Y. Ng:
Inverse Reinforcement Learning. 554-558

Inverse Resolution. 558

Is More General Than. 558

Is More Specific Than. 558

Item. 558

Iterative Classification. 558

J

Junk Email Filtering. 559

K

Shie Mannor:
k-Armed Bandit. 561-563

Xin Jin, Jiawei Han:
K-Means Clustering. 563-564

Xin Jin, Jiawei Han:
K-Medoids Clustering. 564-565

Xin Jin, Jiawei Han:
K-Way Spectral Clustering. 565

Kernel Density Estimation. 566

Kernel Matrix. 566

Xinhua Zhang:
Kernel Methods. 566-570

Kernel Shaping. 570

Kernel-Based Reinforcement\break Learning. 570

Kernels. 570

Kind. 570

Knowledge Discovery. 570

Kohonen Maps. 570

L

L1-Distance. 571

Label. 571

Labeled Data. 571

Language Bias. 571

Laplace Estimate. 571

Latent Class Model. 571

Latent Factor Models and Matrix Factorizations. 571

Geoffrey I. Webb:
Lazy Learning. 571-572

Claude Sammut:
Learning as Search. 572-576

Learning Bayesian Networks. 577

Learning Bias. 577

Learning By Demonstration. 577

Learning By Imitation. 577

Learning Classifier Systems. 577

Learning Control. 577

Learning Control Rules. 577

Claudia Perlich:
Learning Curves in Machine Learning. 577-580

Learning from Complex Data. 580

Learning from Labeled and Unlabeled Data. 584

Learning from Labeled and Unlabeled Data. 580

Learning from Nonpropositional Data. 580

Learning from Nonvectorial Data. 580

Learning from Preferences. 580

Tamás Horváth, Stefan Wrobel:
Learning from Structured Data. 580-584

Kevin B. Korb:
Learning Graphical Models. 584-590

Learning in Logic. 590

Learning in Worlds with Objects. 590

William Stafford Noble, Christina S. Leslie:
Learning Models of Biological Sequences. 590-594

Learning Vector Quantization. 594

Learning with Different Classification Costs. 595

Learning with Hidden Context. 595

Learning Word Senses. 595

Michail G. Lagoudakis:
Least-Squares Reinforcement Learning Methods. 595-600

Leave-One-Out Cross-Validation. 600-601

Leave-One-Out Error. 601

Lessons-Learned Systems. 601

Life-Long Learning. 601

Lifelong Learning. 601

Lift. 601

Novi Quadrianto, Wray L. Buntine:
Linear Discriminant. 601-603

Novi Quadrianto, Wray L. Buntine:
Linear Regression. 603-606

Linear Regression Trees. 606

Linear Separability. 606

Link Analysis. 606

Lise Getoor:
Link Mining and Link Discovery. 606-609

Galileo Namata, Lise Getoor:
Link Prediction. 609-612

Link-Based Classification. 613

Liquid State Machine. 613

Local Distance Metric Adaptation. 613

Local Feature Selection. 613

Xin Jin, Jiawei Han:
Locality Sensitive Hashing Based Clustering. 613

Locally Weighted Learning. 613

Jo-Anne Ting, Sethu Vijayakumar, Stefan Schaal:
Locally Weighted Regression for Control. 613-624

Log-Linear Models. 632

Luc De Raedt:
Logic of Generality. 624-631

Logic Program. 631

Logical Consequence. 631

Logical Regression Tree. 631

Logistic Regression. 631

Logit Model. 631

Long-Term Potentiation of Synapses. 632

LOO Error. 632

Loopy Belief Propagation. 632

Loss. 632

Loss Function. 632

LWPR. 632

LWR. 632

M

m-Estimate. 633

Johannes Fürnkranz:
Machine Learning and Game Playing. 633-637

Philip K. Chan:
Machine Learning for IT Security. 637-639

Susan Craw:
Manhattan Distance. 639

Margin. 639

Market Basket Analysis. 639

Markov Blanket. 639

Markov Chain. 639

Claude Sammut:
Markov Chain Monte Carlo. 639-642

William T. B. Uther:
Markov Decision Processes. 642-646

Markov Model. 646

Markov Net. 646

Markov Network. 646

Markov Process. 646

Markov Random Field. 647

Markovian Decision Rule. 647

Maxent Models. 647

Adwait Ratnaparkhi:
Maximum Entropy Models for Natural Language Processing. 647-651

McDiarmid’s Inequality. 651-652

MCMC. 652

MDL. 652

Mean Absolute Deviation. 652

Mean Absolute Error. 652

Mean Error. 652

Xin Jin, Jiawei Han:
Mean Shift. 652-653

Mean Squared Error. 653

Ying Yang:
Measurement Scales. 653-654

Katharina Morik:
Medicine: Applications of Machine Learning. 654-661

Memory Organization Packets. 661

Memory-Based. 661

Memory-Based Learning. 661

Merge-Purge. 661

Message. 661

Meta-Combiner. 662

Marco Dorigo, Mauro Birattari, Thomas Stützle:
Metaheuristic. 662

Pavel Brazdil, Ricardo Vilalta, Christophe G. Giraud-Carrier, Carlos Soares:
Metalearning. 662-666

Minimum Cuts. 666

Jorma Rissanen:
Minimum Description Length Principle. 666-668

Minimum Encoding Inference. 668

Rohan A. Baxter:
Minimum Message Length. 668-674

Ivan Bruha:
Missing Attribute Values. 674-680

Missing Values. 680

Mistake-Bounded Learning. 680

Mixture Distribution. 680

Rohan A. Baxter:
Mixture Model. 680-682

Mixture Modeling. 683

Mode Analysis. 683

Geoffrey I. Webb:
Model Evaluation. 683

Model Selection. 683

Model Space. 683

Luís Torgo:
Model Trees. 684-686

Arindam Banerjee, Hanhuai Shan:
Model-Based Clustering. 686-689

Model-Based Control. 689

Soumya Ray, Prasad Tadepalli:
Model-Based Reinforcement Learning. 690-693

Modularity Detection. 693

MOO. 693

Morphosyntactic Disambiguation. 693

Most General Hypothesis. 693

Most Similar Point. 694

Most Specific Hypothesis. 694

Yoav Shoham, Rob Powers:
Multi-Agent Learning I: Problem Definition. 694-696

Yoav Shoham, Rob Powers:
Multi-Agent Learning II: Algorithms. 696-699

Multi-Armed Bandit. 699

Multi-Armed Bandit Problem. 699

Multi-Criteria Optimization. 701

Soumya Ray, Stephen Scott, Hendrik Blockeel:
Multi-Instance Learning. 701-710

Multi-Objective Optimization. 710

Luc De Raedt:
Multi-Relational Data Mining. 711

Geoffrey I. Webb:
MultiBoosting. 699-701

Multiple Classifier Systems. 711

Multiple-Instance Learning. 711

Multistrategy Ensemble Learning. 711

Must-Link Constraint. 711

N

Geoffrey I. Webb:
Naïve Bayes. 713-714

NC-Learning. 714

NCL. 714

Eamonn J. Keogh:
Nearest Neighbor. 714-715

Nearest Neighbor Methods. 715

Negative Correlation Learning. 715

Negative Predictive Value. 715-716

Network Analysis. 716

Network Clustering. 716

Networks with Kernel Functions. 716

Neural Network Architecture. 716

Neural Networks. 716

Neuro-Dynamic Programming. 716

Risto Miikkulainen:
Neuroevolution. 716-720

Risto Miikkulainen:
Neuron. 720-721

No-Free-Lunch Theorem. 721

Node. 721

Nogood Learning. 721

Noise. 721

Nominal Attribute. 722

Non-Parametric Methods. 722

Nonparametric Bayesian. 722

Nonparametric Cluster Analysis. 722

Michèle Sebag:
Nonstandard Criteria in Evolutionary Learning. 722-731

Nonstationary Kernels. 731

Nonstationary Kernels Supersmoothing. 731

Normal Distribution. 731

NP-Completeness. 731-732

Numeric Attribute. 732

O

Object. 733

Object Consolidation. 733

Object Space. 733

Hendrik Blockeel:
Observation Language. 733-735

Geoffrey I. Webb:
Occam’s Razor. 735

Ockham’s Razor. 736

Offline Learning. 736

One-Step Reinforcement Learning. 736

Peter Auer:
Online Learning. 736-743

Ontology Learning. 743

Opinion Mining. 743

Optimal Learning. 743

OPUS. 743

Ordered Rule Set. 743

Ordinal Attribute. 743

Out-of-Sample Data. 743

Out-of-Sample Evaluation. 743

Overall and Class-Sensitive Frequencies. 743

Geoffrey I. Webb:
Overfitting. 744

Overtraining. 744

P

PAC Identification. 745

Thomas Zeugmann:
PAC Learning. 745-753

PAC-MDP Learning. 753

Parallel Corpus. 754

Part of Speech Tagging. 754

Pascal Poupart:
Partially Observable Markov Decision Processes. 754-760

James Kennedy:
Particle Swarm Optimization. 760-766

Xin Jin, Jiawei Han:
Partitional Clustering. 766

Passive Learning. 766

PCA. 766

PCFG. 766

Perceptron. 773

Lorenza Saitta, Michèle Sebag:
Phase Transitions in Machine Learning. 767-773

Piecewise Constant Models. 773

Piecewise Linear Models. 773

Plan Recognition. 774

Jan Peters, J. Andrew Bagnell:
Policy Gradient Methods. 774-776

Policy Search. 776

POMDPs. 776

Walter Daelemans:
POS Tagging. 776-779

Positive Definite. 779

Positive Predictive Value. 779

Positive Semidefinite. 779-780

Post-Pruning. 780

Posterior. 780

Geoffrey I. Webb:
Posterior Probability. 780

Postsynaptic Neuron. 780

Pre-Pruning. 795

Kai Ming Ting:
Precision. 780

Kai Ming Ting:
Precision and Recall. 781

Predicate. 781

Predicate Calculus. 781

Predicate Invention. 781-782

Predicate Logic. 782

Prediction with Expert Advice. 782

Predictive Software Models. 782

Jelber Sayyad-Shirabad:
Predictive Techniques in Software Engineering. 782-789

Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. 789-795

Presynaptic Neuron. 795

Principal Component Analysis. 795

Prior. 795

Prior Probabilities. 782

Geoffrey I. Webb:
Prior Probability. 782

Privacy-Preserving Data Mining. 795

Stan Matwin:
Privacy-Related Aspects and Techniques. 795-801

Yasubumi Sakakibara:
Probabilistic Context-Free Grammars. 802-805

Probably Approximately Correct Learning. 805

Process-Based Modeling. 805

Program Synthesis From Examples. 805

Pierre Flener, Ute Schmid:
Programming by Demonstration. 805

Programming by Example. 805

Programming from Traces. 806

Cecilia M. Procopiuc:
Projective Clustering. 806-811

Prolog. 811-812

Property. 812

Propositional Logic. 812

Nicolas Lachiche:
Propositionalization. 812-817

Johannes Fürnkranz:
Pruning. 817

Pruning Set. 817

Q

Peter Stone:
Q-Learning. 819

Quadratic Loss. 819

Qualitative Attribute. 820

Xin Jin, Jiawei Han:
Quality Threshold Clustering. 820

Quantitative Attribute. 820

Sanjay Jain, Frank Stephan:
Query-Based Learning. 820-822

R

Rademacher Average. 823

Rademacher Complexity. 823

Radial Basis Function Approximation. 823

Martin D. Buhmann:
Radial Basis Function Networks. 823-827

Radial Basis Function Neural Networks. 827

Random Decision Forests. 827

Random Forests. 828

Random Subspace Method. 828

Random Subspaces. 828

Randomized Decision Rule. 828

Rank Correlation. 828

Ratio Scale. 828

Real-Time Dynamic Programming. 829

Recall. 829

Receiver Operating Characteristic Analysis. 829

Recognition. 829

Prem Melville, Vikas Sindhwani:
Recommender Systems. 829-838

Record Linkage. 838

Recurrent Associative Memory. 838

Recursive Partitioning. 838

Reference Reconciliation. 838

Novi Quadrianto, Wray L. Buntine:
Regression. 838-842

Luís Torgo:
Regression Trees. 842-845

Xinhua Zhang:
Regularization. 845-849

Regularization Networks. 849

Peter Stone:
Reinforcement Learning. 849-851

Reinforcement Learning in Structured Domains. 851

Relational. 851

Relational Data Mining. 851

Relational Dynamic Programming. 851

Jan Struyf, Hendrik Blockeel:
Relational Learning. 851-857

Relational Regression Tree. 857

Kurt Driessens:
Relational Reinforcement Learning. 857-862

Relational Value Iteration. 862

Relationship Extraction. 862

Relevance Feedback. 862-863

Representation Language. 863

Risto Miikkulainen:
Reservoir Computing. 863

Resolution. 863

Resubstitution Estimate. 863

Reward. 863

Reward Selection. 863

Eric Wiewiora:
Reward Shaping. 863-865

RIPPER. 865

Jan Peters, Russ Tedrake, Nicholas Roy, Jun Morimoto:
Robot Learning. 865-869

Peter A. Flach:
ROC Analysis. 869-875

ROC Convex Hull. 875

ROC Curve. 875

Rotation Forests. 875

RSM. 875

Johannes Fürnkranz:
Rule Learning. 875-879

S

Sample Complexity. 881

Samuel’s Checkers Player. 881

Saturation. 881

SDP. 881

Search Bias. 881

Eric Martin:
Search Engines: Applications of ML. 882-886

Self-Organizing Feature Maps. 886

Samuel Kaski:
Self-Organizing Maps. 886-888

Semantic Mapping. 888

Fei Zheng, Geoffrey I. Webb:
Semi-Naive Bayesian Learning. 889-892

Xiaojin Zhu:
Semi-Supervised Learning. 892-897

Ion Muslea:
Semi-Supervised Text Processing. 897-901

Sensitivity. 901

Kai Ming Ting:
Sensitivity and Specificity. 901-902

Sequence Data. 902

Sequential Data. 902

Sequential Inductive Transfer. 902

Sequential Prediction. 902

Set. 902

Shannon’s Information. 902

Shattering Coefficient. 902

Michail Vlachos:
Similarity Measures. 903-906

Simple Bayes. 906

Risto Miikkulainen:
Simple Recurrent Network. 906

SMT. 906

Solution Concept. 906

Solving Semantic Ambiguity. 906

SOM. 906

SORT. 906

Spam Detection. 906

Specialization. 907

Specificity. 907

Spectral Clustering. 907

Alan Fern:
Speedup Learning. 907-911

Speedup Learning For Planning. 911

Spike-Timing-Dependent Plasticity. 912

Sponsored Search. 912

Squared Error. 912

Squared Error Loss. 912

Stacked Generalization. 912

Stacking. 912

Starting Clause. 912

State. 912

Statistical Learning. 912

Miles Osborne:
Statistical Machine Translation. 912-915

Statistical Natural Language Processing. 916

Statistical Physics Of Learning. 916

Luc De Raedt, Kristian Kersting:
Statistical Relational Learning. 916-924

Thomas Zeugmann:
Stochastic Finite Learning. 925-928

Stratified Cross Validation. 928

Stream Mining. 928-929

String kernel. 929

String Matching Algorithm. 929

Structural Credit Assignment. 929

Xinhua Zhang:
Structural Risk Minimization. 929-930

Structure. 930

Structured Data Clustering. 930

Michael Bain:
Structured Induction. 930-933

Subgroup Discovery. 933

Artur Czumaj, Christian Sohler:
Sublinear Clustering. 933-937

Subspace Clustering. 937

Claude Sammut:
Subsumption. 937-938

Supersmoothing. 938

Petra Kralj Novak, Nada Lavrac, Geoffrey I. Webb:
Supervised Descriptive Rule Induction. 938-941

Supervised Learning. 941

Xinhua Zhang:
Support Vector Machines. 941-946

Swarm Intelligence. 946

Scott Sanner, Kristian Kersting:
Symbolic Dynamic Programming. 946-954

Symbolic Regression. 954

Symmetrization Lemma. 954

Synaptic E.Cacy. 954

T

Tagging. 955

TAN. 955

Taxicab Norm Distance. 955

TD-Gammon. 955-956

TDIDT Strategy. 956

Temporal Credit Assignment. 956

Temporal Data. 956

William T. B. Uther:
Temporal Difference Learning. 956-962

Test Data. 962

Test Instances. 962

Test Set. 962

Test Time. 962

Test-Based Coevolution. 962

Text Clustering. 962

Text Learning. 962

Dunja Mladenic:
Text Mining. 962-963

Massimiliano Ciaramita:
Text Mining for Advertising. 963-968

Bettina Berendt:
Text Mining for News and Blogs Analysis. 968-972

Aleksander Kolcz:
Text Mining for Spam Filtering. 972-978

Marko Grobelnik, Dunja Mladenic, Michael Witbrock:
Text Mining for the Semantic Web. 978-980

Text Spatialization. 980

John Risch, Shawn Bohn, Steve Poteet, Anne Kao, Lesley Quach, Yuan-Jye Jason Wu:
Text Visualization. 980-986

TF-IDF. 986-987

Threshold Phenomena in Learning. 987

Time Sequence. 987

Eamonn J. Keogh:
Time Series. 987-988

Topic Mapping. 988

Risto Miikkulainen:
Topology of a Neural Network. 988-989

Pierre Flener, Ute Schmid:
Trace-Based Programming. 989

Training Curve. 989

Training Data. 989

Training Examples. 989

Training Instances. 990

Training Set. 990

Training Time. 990

Trait. 990

Trajectory Data. 990

Transductive Learning. 990

Transfer of Knowledge Across Domains. 990

Transition Probabilities. 990

Fei Zheng, Geoffrey I. Webb:
Tree Augmented Naive Bayes. 990-991

Siegfried Nijssen:
Tree Mining. 991-999

Tree-Based Regression. 999

True Negative. 999

True Negative Rate. 999

True Positive. 999

True Positive Rate. 999

Type. 999

Typical Complexity of Learning. 999

U

Underlying Objective. 1001

Unit. 1001

Marcus Hutter:
Universal Learning Theory. 1001-1008

Unknown Attribute Values. 1008

Unknown Values. 1008

Unlabeled Data. 1008

Unsolicited Commercial Email Filtering. 1008

Unstable Learner. 1008-1009

Unsupervised Learning. 1009

Unsupervised Learning on Document Datasets. 1009

Utility Problem. 1009

V

Michail G. Lagoudakis:
Value Function Approximation. 1011-1021

Variable Selection. 1021

Variable Subset Selection. 1021

Variance. 1021

Variance Hint. 1021

Thomas Zeugmann:
VC Dimension. 1021-1024

Vector Optimization. 1024

Claude Sammut:
Version Space. 1024-1025

Viterbi Algorithm. 1025

W

Web Advertising. 1027

Risto Miikkulainen:
Weight. 1027

Within-Sample Evaluation. 1027

Rada Mihalcea:
Word Sense Disambiguation. 1027-1030

Word Sense Discrimination. 1030

Z

Zero-One Loss. 1031