• 
  Claude Sammut, Geoffrey I. Webb (Eds.): Encyclopedia of Machine Learning. Springer 2010 ISBN 978-0-387-30768-8

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
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  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
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  AQ. 40
• 
  Area Under Curve. 40
• 
  ARL. 40
• 
  ART. 40
• 
  ARTDP. 40
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  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
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  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
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  Bias-Variance Trade-offs. 110
• 
  Bias-Variance-Covariance Decomposition. 111
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  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
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  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
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  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
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  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
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  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
•