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ORCIDEncyclopedia of Machine Learning 2010
- Claude Sammut, Geoffrey I. Webb:
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
- 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 H. 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 Bezerra de Andrade e 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 J. 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
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