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