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ORCIDEncyclopedia of Machine Learning and Data Mining 2017
- Claude Sammut, Geoffrey I. Webb:
Encyclopedia of Machine Learning and Data Mining. Springer 2017, ISBN 978-1-4899-7685-7
A
- A/B Testing. 1
- Antonis C. Kakas:
Abduction. 1-8 - Absolute Error Loss. 8
- Accuracy. 8
- ACO. 8
- Actions. 9
- David Cohn:
Active Learning. 9-14 - Sanjoy Dasgupta:
Active Learning Theory. 14-19 - Adaboost. 19-20
- Adaptive Control Processes. 20
- Adaptive Learning. 20
- Andrew G. Barto:
Adaptive Real-Time Dynamic Programming. 20-23 - Gail A. Carpenter, Stephen Grossberg:
Adaptive Resonance Theory. 24-40 - Adaptive System. 40
- Agent. 40
- Agent-Based Computational Models. 40
- Agent-Based Modeling and Simulation. 40
- Agent-Based Simulation Models. 40
- AIS. 40
- Geoffrey I. Webb:
Algorithm Evaluation. 40-41 - Analogical Reasoning. 41
- Analysis of Text. 41
- Analytical Learning. 41
- Varun Chandola, Arindam Banerjee, Vipin Kumar:
Active Learning. 42-56 - Marco Dorigo, Mauro Birattari:
Ant Colony Optimization. 56-59 - Anytime Algorithm. 59
- AODE. 60
- Apprenticeship Learning. 60
- Approximate Dynamic Programming. 60
- Hannu Toivonen
:
Apriori Algorithm. 60 - AQ. 61
- Architecture. 61
- Area Under Curve. 61
- ARL. 61
- ART. 61
- ARTDP. 61
- Jon Timmis:
Artificial Immune Systems. 61-65 - Artificial Life. 65
- Artificial Neural Networks. 65-66
- Jürgen Branke:
Artificial Societies. 66-70 - Assertion. 70
- Assessment of Model Performance. 70
- Hannu Toivonen:
Association Rule. 70-71 - Associative Bandit Problem. 71
- Alexander L. Strehl:
Associative Reinforcement Learning. 71-73 - Chris Drummond:
Attribute. 73-75 - Attribute Selection. 75
- Attribute-Value Learning. 75
- AUC. 75
- Authority Control. 75
- Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Autonomous Helicopter Flight Using Reinforcement Learning. 75-85 - Average-Cost Neuro-Dynamic Programming. 85
- Average-Cost Optimization. 85
- Fei Zheng, Geoffrey I. Webb:
Averaged One-Dependence Estimators. 85-87 - Average-Payoff Reinforcement Learning. 87
- Prasad Tadepalli:
Average-Reward Reinforcement Learning. 87-92
B
- Backprop. 93
- Paul W. Munro:
Backpropagation. 93-97 - Bagging. 97-98
- Bake-Off. 98
- Bandit Problem with Side Information. 98
- Bandit Problem with Side Observations. 98
- Basic Lemma. 98
- Hannu Toivonen:
Basket Analysis. 98 - Batch Learning. 98-99
- Baum-Welch Algorithm. 99
- Bayes Adaptive Markov Decision Processes. 99
- Bayes Net. 99
- Geoffrey I. Webb:
Bayes' Rule. 99 - Bayes' Theorem. 100
- Wray L. Buntine:
Bayesian Methods. 100-106 - Bayesian Model Averaging. 106
- Bayesian Network. 106-107
- Peter Orbanz, Yee Whye Teh:
Bayesian Nonparametric Models. 107-116 - Pascal Poupart:
Bayesian Reinforcement Learning. 116-120 - Claude Sammut:
Beam Search. 120 - Claude Sammut:
Behavioral Cloning. 120-124 - Belief State Markov Decision Processes. 125
- Bellman Equation. 125
- Bias. 125
- Hendrik Blockeel:
Bias Specification Language. 125-128 - Bias Variance Decomposition. 128-129
- Dev G. Rajnarayan, David H. Wolpert:
Bias-Variance Trade-Offs: Novel Applications. 129-139 - Bias-Variance-Covariance Decomposition. 139-140
- Bilingual Lexicon Extraction. 140
- Binning. 140
- Wulfram Gerstner:
Biological Learning: Synaptic Plasticity, Hebb Rule and Spike Timing Dependent Plasticity. 140-143 - C. David Page, Sriraam Natarajan:
Biomedical Informatics. 143-163 - Blog Mining. 163-164
- Geoffrey E. Hinton:
Boltzmann Machines. 164-168 - Boosting. 168
- Bootstrap Sampling. 168
- Bottom Clause. 169
- Bounded Differences Inequality. 169
- BP. 169
- Breakeven Point. 169
C
- Candidate-Elimination Algorithm. 171
- Cannot-Link Constraint. 171
- Thomas R. Shultz, Scott E. Fahlman:
Cascade Correlation. 171-180 - Cascor. 180
- Case. 180
- Case-Based Learning. 180
- Susan Craw:
Case-Based Reasoning. 180-188 - Categorical Attribute. 188
- Periklis Andritsos, Panayiotis Tsaparas:
Categorical Data Clustering. 188-193 - Categorization. 194
- Category. 194
- Causal Discovery. 194
- Ricardo Silva:
Causality. 194-202 - CC. 202
- Certainty Equivalence Principle. 202
- Characteristic. 202
- Citation or Reference Matching (When Applied to Bibliographic Data). 202
- City Block Distance. 202
- Chris Drummond:
Class. 202-203 - Johannes Fürnkranz:
Class Binarization. 203-204 - Charles X. Ling, Victor S. Sheng:
Class Imbalance Problem. 204-205 - Chris Drummond:
Classification. 205-208 - Classification Algorithms. 208-209
- Classification Learning. 209
- Johannes Fürnkranz:
Classification Rule. 209 - Classification Tree. 209
- Peter A. Flach:
Classifier Calibration. 210-217 - Pier Luca Lanzi:
Classifier Systems. 217-224 - Clause. 224-225
- Clause Learning. 225
- Click-Through Rate (CTR). 225
- Clonal Selection. 225
- Closest Point. 225
- Cluster Editing. 225-226
- Cluster Ensembles. 226
- Cluster Initialization. 226
- Cluster Optimization. 226
- Clustering. 226
- Clustering Aggregation. 226
- Clustering Ensembles. 226
- João Gama:
Clustering from Data Streams. 226-231 - Clustering of Nonnumerical Data. 231
- Clustering with Advice. 231
- Clustering with Constraints. 231
- Clustering with Qualitative Information. 231
- Clustering with Side Information. 231
- Coevolution. 231
- Coevolutionary Computation. 231
- R. Paul Wiegand:
Coevolutionary Learning. 232-237 - Collaborative Filtering. 237
- Collection. 237
- Galileo Namata, Prithviraj Sen, Mustafa Bilgic, Lise Getoor:
Collective Classification. 238-242 - Commercial Email Filtering. 242
- Committee Machines. 242
- Community Detection. 242
- Comparable Corpus. 243
- Comparison Training. 243
- Competitive Coevolution. 243
- Competitive Learning. 243
- Complex Adaptive System. 243
- Jun He:
Complexity in Adaptive Systems. 243-247 - Sanjay Jain, Frank Stephan:
Complexity of Inductive Inference. 247-251 - Compositional Coevolution. 251
- Sanjay Jain, Frank Stephan:
Computational Complexity of Learning. 251-253 - Computational Discovery of Quantitative Laws. 253
- Claude Sammut, Michael Bonnell Harries:
Concept Drift. 253-256 - Claude Sammut:
Concept Learning. 256-259 - Conditional Random Field. 259
- Confirmation Theory. 260
- Kai Ming Ting:
Confusion Matrix. 260 - Bernhard Pfahringer:
Conjunctive Normal Form. 260-261 - Connection Strength. 261
- John Case, Sanjay Jain:
Connections Between Inductive Inference and Machine Learning. 261-272 - Connectivity. 272
- Consensus Clustering. 272
- Kiri L. Wagstaff:
Constrained Clustering. 272-274 - Constraint Classification. 274
- Siegfried Nijssen:
Constraint-Based Mining. 274-279 - Constructive Induction. 279
- Content Match. 279
- Content-Based Filtering. 279
- Content-Based Recommending. 279
- Context-Sensitive Learning. 279
- Contextual Advertising. 279
- Continual Learning. 279-280
- Continuous Attribute. 280
- Contrast Set Mining. 280
- Cooperative Coevolution. 280
- Co-reference Resolution. 280
- Anthony Wirth:
Correlation Clustering. 280-284 - Correlation-Based Learning. 285
- Cost. 285
- Cost Function. 285
- Cost-Sensitive Classification. 285
- Charles X. Ling, Victor S. Sheng:
Cost-Sensitive Learning. 285-289 - Cost-to-Go Function Approximation. 289
- Co-training. 289
- Xinhua Zhang:
Covariance Matrix. 290-293 - Johannes Fürnkranz:
Covering Algorithm. 293-294 - Claude Sammut:
Credit Assignment. 294-298 - Cross-Language Document Categorization. 298
- Cross-Language Information Retrieval. 298-299
- Cross-Language Question Answering. 299
- Nicola Cancedda, Jean-Michel Renders:
Cross-Lingual Text Mining. 299-306 - Cross-Validation. 306
- Pietro Michelucci, Daniel Oblinger:
Cumulative Learning. 306-314 - Eamonn J. Keogh, Abdullah Mueen:
Curse of Dimensionality. 314-315
D
- Data Augmentation. 317
- Data Cleaning. 317
- Data Cleansing. 317
- Data Enrichment. 317
- Data Integration. 317
- Data Linkage. 317
- Data Matching. 317
- Data mining on Text. 317
- Zahraa Said Abdallah, Lan Du, Geoffrey I. Webb:
Data Preparation. 318-327 - Data Preprocessing. 327
- Data Scrubbing. 327
- Data Reconciliation. 327
- Data Set. 327
- Data Wrangling. 327
- DBN. 328
- Decision Epoch. 328
- Johannes Fürnkranz:
Decision List. 328 - Johannes Fürnkranz:
Decision Lists and Decision Trees. 328-329 - Decision Rule. 330
- Johannes Fürnkranz:
Decision Stump. 330 - Decision Threshold. 330
- Johannes Fürnkranz:
Decision Tree. 330-335 - Decision Trees for Regression. 335
- Deductive Learning. 335
- Deduplication. 335
- Deduplication or Duplicate Detection (When Applied to One Database Only). 335
- Geoffrey E. Hinton:
Deep Belief Nets. 335-338 - Deep Belief Networks. 338
- Jürgen Schmidhuber:
Deep Learning. 338-348 - Claude Sammut:
Density Estimation. 348-349 - Jörg Sander:
Density-Based Clustering. 349-353 - Dependency Directed Backtracking. 353
- Detail. 353
- Diagonal Matrix. 353
- Differential Prediction. 353
- Digraphs. 353-354
- Michail Vlachos:
Dimensionality Reduction. 354-361 - Dimensionality Reduction on Text via Feature Selection. 361
- Directed Graphs. 361
- Yee Whye Teh:
Dirichlet Process. 361-370 - Discrete Attribute. 370
- Ying Yang:
Discretization. 370-371 - Discriminative Learning. 371
- Bernhard Pfahringer:
Disjunctive Normal Form. 371-372 - Distance. 372
- Distance Functions. 372
- Distance Measures. 372
- Distance Metrics. 372
- Distribution-Free Learning. 372
- Johannes Fürnkranz:
Divide-and-Conquer Learning. 372 - Document Categorization. 372
- Dunja Mladenic, Janez Brank, Marko Grobelnik:
Document Classification. 372-377 - Domain Adaptation. 377
- Dual Control. 377
- Duplicate Detection. 377
- Dynamic Bayesian Network. 377
- Dynamic Decision Networks. 377
- Martin L. Puterman, Jonathan Patrick:
Dynamic Programming. 377-388 - Dynamic Programming for Relational Domains. 388
- Dynamic Selection of Bias. 388
- Dynamic Systems. 388
E
- EBL. 389
- Echo State Network. 389
- ECOC. 389
- Edge Prediction. 389
- John Langford:
Efficient Exploration in Reinforcement Learning. 389-392 - EFSC. 392
- Eigenvector. 392
- Elman Network. 392
- Embodied Evolutionary Learning. 392
- Emerging Patterns. 392
- Xinhua Zhang:
Empirical Risk Minimization. 392-393 - Gavin Brown:
Ensemble Learning. 393-402 - Entailment. 402
- Indrajit Bhattacharya, Lise Getoor:
Entity Resolution. 402-408 - EP. 408
- Thomas Zeugmann:
Epsilon Cover. 408-409 - Thomas Zeugmann:
Epsilon Nets. 409-410 - Ljupco Todorovski:
Equation Discovery. 410-414 - Error. 414
- Error Correcting Output Codes. 414
- Error Curve. 414
- Kai Ming Ting:
Error Rate. 414 - Error Squared. 415
- Error-Correcting Output Codes (ECOC). 415
- Estimation of Density Level Sets. 415
- Evaluation. 415
- Evaluation Data. 415
- Geoffrey I. Webb:
Evaluation of Learning Algorithms. 415-416 - Evaluation of Model Performance. 416
- Evaluation Set. 416
- Gregor Leban, Blaz Fortuna, Marko Grobelnik:
Event Extraction from Media Texts. 416-422 - Evolution of Agent Behaviors. 422
- Evolution of Robot Control. 422
- Evolutionary Algorithms. 422-423
- David W. Corne, Julia Handl, Joshua D. Knowles:
Evolutionary Clustering. 423-429 - Evolutionary Computation. 429
- Biliana Alexandrova-Kabadjova, Alma Lilia García-Almanza, Serafín Martínez-Jaramillo:
Evolutionary Computation in Economics. 429-434 - Serafín Martínez-Jaramillo, Tonatiuh Peña Centeno, Biliana Alexandrova-Kabadjova:
Evolutionary Computation in Finance. 435-444 - Alma Lilia García-Almanza, Biliana Alexandrova-Kabadjova, Serafín Martínez-Jaramillo:
Evolutionary Computational Techniques in Marketing. 444-446 - Evolutionary Computing. 446
- Evolutionary Constructive Induction. 446
- Evolutionary Feature Selection. 446
- Krzysztof Krawiec:
Evolutionary Feature Selection and Construction. 447-451 - Evolutionary Feature Synthesis. 451
- Carlos Kavka:
Evolutionary Fuzzy Systems. 451-457 - Moshe Sipper:
Evolutionary Games. 457-465 - Evolutionary Grouping. 465
- Christian Igel:
Evolutionary Kernel Learning. 465-469 - Phil Husbands:
Evolutionary Robotics. 469-480 - Evolving Neural Networks. 480
- Example. 480
- Example Space. 480
- Example-Based Programming. 480
- Xin Jin, Jiawei Han:
Expectation Maximization Clustering. 480-482 - Tom Heskes:
Expectation Propagation. 482-487 - Experience Curve. 487
- Experience-Based Reasoning. 487
- Explanation. 487
- Explanation-Based Generalization for Planning. 487
- Gerald DeJong, Shiau Hong Lim:
Explanation-Based Learning. 487-492 - Subbarao Kambhampati, Sung Wook Yoon:
Explanation-Based Learning for Planning. 492-496
F
- F1-Measure. 497
- False Negative. 497
- False Positive. 497
- Feature. 497
- Janez Brank, Dunja Mladenic, Marko Grobelnik:
Feature Construction in Text Mining. 498-503 - Feature Generation in Text Mining. 503
- Feature Projection. 503
- Suhang Wang, Jiliang Tang, Huan Liu:
Feature Selection. 503-511 - Dunja Mladenic:
Feature Selection in Text Mining. 511-515 - Feature Subset Selection. 515
- Feature Weighting. 515
- Feedforward Recurrent Network. 515
- Field Scrubbing. 515
- Finite Mixture Model. 515
- Peter A. Flach:
First-Order Logic. 515-521 - First-Order Predicate Calculus. 521
- First-Order Predicate Logic. 521
- First-Order Regression Tree. 521
- Gemma C. Garriga:
Formal Concept Analysis. 522-523 - Hannu Toivonen:
Frequent Itemset. 523-524 - Hannu Toivonen:
Frequent Pattern. 524-529 - Frequent Set. 529
- Functional Trees. 529
- Fuzzy Sets. 529
- Fuzzy Systems. 529-530
G
- Xinhua Zhang:
Gaussian Distribution. 531-535 - Novi Quadrianto, Kristian Kersting, Zhao Xu:
Gaussian Process. 535-548 - Yaakov Engel:
Gaussian Process Reinforcement Learning. 548-556 - Gaussian Processes. 556
- Generality and Logic. 556
- Claude Sammut:
Generalization. 556 - Mark Reid:
Generalization Bounds. 556 - Generalization Performance. 564
- Generalized Delta Rule. 564
- General-to-Specific Search. 564
- Bin Liu, Geoffrey I. Webb:
Generative and Discriminative Learning. 565-566 - Generative Learning. 566
- Claude Sammut:
Genetic and Evolutionary Algorithms. 566-567 - Genetic Attribute Construction. 568
- Genetic Clustering. 568
- Genetic Feature Selection. 568
- Genetic Grouping. 568
- Genetic Neural Networks. 568
- Moshe Sipper:
Genetic Programming. 568 - Genetics-Based Machine Learning. 568
- Gibbs Sampling. 568
- Gini Coefficient. 568
- Gram Matrix. 569
- Grammar Learning. 569
- Lorenza Saitta, Michèle Sebag:
Grammatical Inference. 569-570 - Grammatical Tagging. 570
- Charu C. Aggarwal:
Graph Clustering. 570-579 - Thomas Gärtner, Tamás Horváth, Stefan Wrobel:
Graph Kernels. 579-581 - Deepayan Chakrabarti:
Graph Mining. 581-584 - Julian J. McAuley, Tibério S. Caetano, Wray L. Buntine:
Graphical Models. 584-592 - Tommy R. Jensen:
Graphs. 592-596 - Claude Sammut:
Greedy Search. 596 - Lawrence Holder:
Greedy Search Approach of Graph Mining. 597-603 - Hossam Sharara, Lise Getoor:
Group Detection. 603-607 - Grouping. 607
- Growing Set. 607
- Growth Function. 607
H
- Hebb Rule. 609
- Hebbian Learning. 609
- Heuristic Rewards. 609
- Antal van den Bosch:
Hidden Markov Models. 609-611 - Bernhard Hengst:
Hierarchical Reinforcement Learning. 611-619 - John Lloyd:
Higher-Order Logic. 619-624 - Hold-One-Out Error. 624
- Holdout Data. 624
- Holdout Evaluation. 624
- Holdout Set. 624
- Risto Miikkulainen:
Hopfield Network. 625 - Hyperparameter Optimization. 625
- Hendrik Blockeel:
Hypothesis Language. 625-629 - Hendrik Blockeel:
Hypothesis Space. 629-632
I
- Identification. 633
- Identity Uncertainty. 633
- Idiot's Bayes. 633
- Immune Computing. 633
- Immune Network. 633
- Immune-Inspired Computing. 633
- Immunocomputing. 633
- Immunological Computation. 633
- Implication. 634
- Improvement Curve. 634
- Paul E. Utgoff:
Incremental Learning. 634-637 - Indirect Reinforcement Learning. 637
- James Cussens:
Induction. 637-640 - Induction as Inverted Deduction. 640
- Inductive Bias. 641
- Stefan Kramer:
Inductive Database Approach to Graphmining. 641-642 - Sanjay Jain, Frank Stephan:
Inductive Inference. 642-648 - Inductive Inference Rules. 648
- Inductive Learning. 648
- Luc De Raedt:
Inductive Logic Programming. 648-656 - Ljupco Todorovski:
Inductive Process Modeling. 656-658 - Inductive Program Synthesis. 658
- Pierre Flener, Ute Schmid:
Inductive Programming. 658-666 - Inductive Synthesis. 666
- Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares:
Inductive Transfer. 666-671 - Inequalities. 671
- Information Retrieval. 671-672
- In-Sample Evaluation. 672
- Instance. 672
- Instance Language. 672
- Instance Space. 672
- Eamonn J. Keogh:
Instance-Based Learning. 672-673 - William D. Smart:
Instance-Based Reinforcement Learning. 673-677 - Intelligent Backtracking. 677
- Intent Recognition. 677
- Internal Model Control. 677
- Interval Scale. 677
- Inverse Entailment. 677-678
- Inverse Optimal Control. 678
- Pieter Abbeel, Andrew Y. Ng:
Inverse Reinforcement Learning. 678-682 - Inverse Resolution. 682-683
- Is More General Than. 683
- Is More Specific Than. 683
- Isotonic Calibration. 683
- Item. 683
- Item Space. 683
- Iterative Algorithm. 683
- Iterative Classification. 683
- Iterative Computation. 683
J
- Junk Email Filtering. 685
K
- Shie Mannor:
k-Armed Bandit. 687-690 - Kernel Density Estimation. 690
- Kernel Matrix. 690
- Xinhua Zhang:
Kernel Methods. 690-695 - Kernel Shaping. 695
- Kernel-Based Reinforcement Learning. 695
- Kernels. 695
- Kind. 695
- Xin Jin, Jiawei Han:
K-Means Clustering. 695-697 - Xin Jin, Jiawei Han:
K-Medoids Clustering. 697-700 - Kohonen Maps. 700
- Xin Jin, Jiawei Han:
K-Way Spectral Clustering. 700
L
- L1-Distance. 701
- Label. 701
- Labeled Data. 701
- Language Bias. 701
- Laplace Estimate. 701
- Laplacian Matrix. 701
- Latent Class Model. 701
- Latent Factor Models and Matrix Factorizations. 701-702
- Geoffrey I. Webb:
Lazy Learning. 702 - Learning Algorithm Evaluation. 703
- Claude Sammut:
Learning as Search. 703-708 - Learning Bayesian Networks. 708
- Learning Bias. 708
- Learning by Demonstration. 708
- Learning by Imitation. 708
- Learning Classifier Systems. 708
- Learning Control. 708
- Learning Control Rules. 708
- Claudia Perlich:
Learning Curves in Machine Learning. 708-711 - Learning from Complex Data. 711
- Learning from Labeled and Unlabeled Data. 711
- Learning from Non-Propositional Data. 711
- Learning from Nonvectorial Data. 711
- Learning from Preferences. 711
- Tamás Horváth, Stefan Wrobel:
Learning from Structured Data. 712-715 - Kevin B. Korb:
Learning Graphical Models. 715-723 - Learning in Logic. 723
- Learning in Worlds with Objects. 723
- William Stafford Noble, Christina S. Leslie:
Learning Models of Biological Sequences. 723-729 - Learning to Learn. 729
- Hang Li:
Learning to Rank. 729-734 - Viktoriia Sharmanska, Novi Quadrianto:
Learning Using Privileged Information. 734-737 - Learning Vector Quantization. 737
- Learning with Different Classification Costs. 737
- Learning with Hidden Context. 738
- Learning Word Senses. 738
- Michail G. Lagoudakis:
Least-Squares Reinforcement Learning Methods. 738-744 - Leave-One-Out Cross-Validation. 744
- Leave-One-Out Error. 744
- Lessons-Learned Systems. 744
- Lifelong Learning. 744
- Life-Long Learning. 744
- Lift. 744-745
- Novi Quadrianto, Wray L. Buntine:
Linear Discriminant. 745-747 - Novi Quadrianto, Wray L. Buntine:
Linear Regression. 747-750 - Linear Regression Trees. 751
- Linear Separability. 751
- Link Analysis. 751
- Lise Getoor:
Link Mining and Link Discovery. 751-753 - Galileo Namata, Lise Getoor:
Link Prediction. 753-758 - Link-Based Classification. 758
- Liquid State Machine. 758
- List Washing. 758
- Local Distance Metric Adaptation. 758
- Local Feature Selection. 758
- Xin Jin, Jiawei Han:
Locality Sensitive Hashing Based Clustering. 758-759 - Locally Weighted Learning. 759
- Jo-Anne Ting, Franziska Meier, Sethu Vijayakumar, Stefan Schaal:
Locally Weighted Regression for Control. 759-772 - Luc De Raedt:
Logic of Generality. 772-780 - Logic Program. 780
- Logical Consequence. 780
- Logical Regression Tree. 780
- Logistic Calibration. 780
- Logistic Regression. 780-781
- Logit Model. 781
- Log-Linear Models. 781
- Long-Term Potentiation of Synapses. 781
- LOO Error. 781
- Loopy Belief Propagation. 781
- Loss. 781
- Loss Function. 781
- Lossy Compression. 781
- LVQ. 781
- LWPR. 781
- LWR. 781
M
- Johannes Fürnkranz:
Machine Learning and Game Playing. 783-788 - Philip K. Chan:
Machine Learning for IT Security. 788-790 - Susan Craw:
Manhattan Distance. 790-791 - Margin. 791
- Market Basket Analysis. 791
- Markov Chain. 791
- Claude Sammut:
Markov Chain Monte Carlo. 791-793 - William T. B. Uther:
Markov Decision Processes. 793-798 - Markov Model. 798
- Markov Net. 798
- Markov Network. 799
- Markov Process. 799
- Markov Random Field. 799
- Markovian Decision Rule. 799
- Maxent Models. 799
- Maximally General Hypothesis. 799
- Maximally Specific Hypothesis. 799
- Adwait Ratnaparkhi:
Maximum Entropy Models for Natural Language Processing. 800-805 - McDiarmid's Inequality. 805
- MCMC. 805
- Mean Absolute Deviation. 805
- Mean Absolute Error. 806
- Mean Error. 806
- Xin Jin, Jiawei Han:
Mean Shift. 806-808 - Mean Squared Error. 808
- Ying Yang:
Measurement Scales. 808-809 - Katharina Morik:
Medicine: Applications of Machine Learning. 809-817 - Memory-Based. 817
- Memory-Based Learning. 817
- Merge-Purge. 817
- Message. 817
- Meta-combiner. 817
- Marco Dorigo, Mauro Birattari, Thomas Stützle:
Metaheuristic. 817-818 - Pavel Brazdil, Ricardo Vilalta, Christophe G. Giraud-Carrier, Carlos Soares:
Metalearning. 818-823 - Minimum Cuts. 823
- Teemu Roos:
Minimum Description Length Principle. 823-827 - Rohan A. Baxter:
Minimum Message Length. 827-834 - Mining a Stream of Opinionated Documents. 834
- Ivan Bruha:
Missing Attribute Values. 834-841 - Missing Values. 841
- Mistake-Bounded Learning. 841
- Mixture Distribution. 841
- Rohan A. Baxter:
Mixture Model. 841-844 - Mixture Modeling. 844
- Mode Analysis. 844
- Model Assessment. 844
- Geoffrey I. Webb:
Model Evaluation. 844-845 - Model Selection. 845
- Model Space. 845
- Luís Torgo:
Model Trees. 845-848 - Arindam Banerjee, Hanhuai Shan:
Model-Based Clustering. 848-852 - Model-Based Control. 852
- Soumya Ray, Prasad Tadepalli:
Model-Based Reinforcement Learning. 852-855 - Modularity Detection. 856
- MOO. 856
- Morphosyntactic Disambiguation. 856
- Most General Hypothesis. 856
- Most Similar Point. 857
- Most Specific Hypothesis. 857
- Yoav Shoham, Rob Powers:
Multi-agent Learning. 857-860 - Yoav Shoham, Rob Powers:
Multi-agent Learning Algorithms. 860-863 - Multi-armed Bandit. 863
- Multi-armed Bandit Problem. 863
- Geoffrey I. Webb:
MultiBoosting. 863-864 - Multi-criteria Optimization. 864
- Soumya Ray, Stephen Scott, Hendrik Blockeel:
Multi-Instance Learning. 864-875 - Zhi-Hua Zhou, Min-Ling Zhang:
Multi-label Learning. 875-881 - Multi-objective Optimization. 881-882
- Multiple Classifier Systems. 882
- Soumya Ray, Stephen Scott, Hendrik Blockeel:
Multiple-Instance Learning. 882-892 - Luc De Raedt:
Multi-relational Data Mining. 892-893 - Multistrategy Ensemble Learning. 893
- Multitask Learning. 893
- Must-Link Constraint. 893
N
- Geoffrey I. Webb:
Naïve Bayes. 895-896 - NCL. 896
- NC-Learning. 896
- Eamonn J. Keogh:
Nearest Neighbor. 897 - Nearest Neighbor Methods. 897
- Negative Correlation Learning. 897-898
- Negative Predictive Value. 898
- Net Lift Modeling. 898
- Network Analysis. 898
- Network Clustering. 898
- Networks with Kernel Functions. 898
- Neural Networks. 898-899
- Neuro-Dynamic Programming. 899
- Risto Miikkulainen:
Neuroevolution. 899-904 - Risto Miikkulainen:
Neuron. 904-905 - Node. 905
- No-Free-Lunch Theorem. 905
- Nogood Learning. 905
- Noise. 905
- Nominal Attribute. 905
- Nonparametric Bayesian. 905
- Nonparametric Cluster Analysis. 905
- Non-Parametric Methods. 906
- Michèle Sebag:
Nonstandard Criteria in Evolutionary Learning. 906-916 - Nonstationary Kernels. 916
- Normal Distribution. 916
- NP-Completeness. 916
- Numeric Attribute. 916
O
- Object. 917
- Object Consolidation. 917
- Object Identification. 917
- Object Matching. 917
- Object Space. 917
- Objective Function. 917
- Hendrik Blockeel:
Observation Language. 917-920 - Geoffrey I. Webb:
Occam's Razor. 920-921 - Ockham's Razor. 921
- Offline Learning. 921
- One-Against-All Training. 921
- One-Against-One Training. 921
- 1-Norm Distance. 921
- One-Step Reinforcement Learning. 921
- Ron Kohavi, Roger Longbotham:
Online Controlled Experiments and A/B Testing. 922-929 - Peter Auer:
Online Learning. 929-937 - Ontology Learning. 937-938
- Opinion Extraction. 938
- Opinion Mining. 938
- Myra Spiliopoulou, Eirini Ntoutsi, Max Zimmermann:
Opinion Stream Mining. 938-947 - Optimal Learning. 947
- Ordered Rule Set. 947
- Ordinal Attribute. 947
- Out-of-Sample Data. 947
- Out-of-Sample Evaluation. 947
- Overall and Class-Sensitive Frequencies. 947
- Geoffrey I. Webb:
Overfitting. 947-948 - Overtraining. 948
P
- PAC Identification. 949
- Thomas Zeugmann:
PAC Learning. 949-959 - PAC-MDP Learning. 959
- Pairwise Classification. 959
- Parallel Corpus. 959
- Part of Speech Tagging. 959
- Pascal Poupart:
Partially Observable Markov Decision Processes. 959-966 - James Kennedy:
Particle Swarm Optimization. 967-972 - Xin Jin, Jiawei Han:
Partitional Clustering. 973-974 - Passive Learning. 974
- PCA. 974
- PCFG. 974
- Lorenza Saitta, Michèle Sebag:
Phase Transitions in Machine Learning. 974-982 - Piecewise Constant Models. 982
- Piecewise Linear Models. 982
- Plan Recognition. 982
- Polarity Learning on a Stream. 982
- Jan Peters, J. Andrew Bagnell:
Policy Gradient Methods. 982-985 - Policy Search. 985
- POMDPs. 985
- Walter Daelemans:
POS Tagging. 985-989 - Positive Definite. 989
- Positive Predictive Value. 989
- Positive Semidefinite. 989
- Posterior. 989
- Geoffrey I. Webb:
Posterior Probability. 989-990 - Post-pruning. 990
- Postsynaptic Neuron. 990
- Kai Ming Ting:
Precision. 990 - Kai Ming Ting:
Precision and Recall. 990-991 - Predicate. 991
- Predicate Calculus. 991
- Predicate Invention. 991
- Predicate Logic. 991
- Prediction with Expert Advice. 992
- Predictive Software Models. 992
- Jelber Sayyad-Shirabad:
Predictive Techniques in Software Engineering. 992-1000 - Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. 1000-1005 - Pre-pruning. 1005-1006
- Presynaptic Neuron. 1006
- Principal Component Analysis. 1006
- Prior. 1006
- Geoffrey I. Webb:
Prior Probability. 1006 - Privacy-Preserving Data Mining. 1006
- Stan Matwin:
Privacy-Related Aspects and Techniques. 1006-1013 - Yasubumi Sakakibara:
Probabilistic Context-Free Grammars. 1013-1017 - Probability Calibration. 1017
- Probably Approximately Correct Learning. 1017
- Process-Based Modeling. 1017
- Program Synthesis from Examples. 1017
- Pierre Flener, Ute Schmid:
Programming by Demonstration. 1017-1018 - Programming by Example (PBE). 1018
- Programming by Examples. 1018
- Programming from Traces. 1018
- Cecilia M. Procopiuc:
Projective Clustering. 1018-1025 - Prolog. 1025
- Property. 1025
- Propositional Logic. 1025
- Nicolas Lachiche:
Propositionalization. 1025-1031 - Prospective Evaluation. 1031
- Johannes Fürnkranz:
Pruning. 1031-1032 - Pruning Set. 1032
Q
- Peter Stone:
Q-Learning. 1033 - Quadratic Loss. 1033
- Qualitative Attribute. 1033
- Quality Threshold. 1033
- Xin Jin, Jiawei Han:
Quality Threshold Clustering. 1033-1034 - Quantitative Attribute. 1034
- Maria Schuld, Francesco Petruccione:
Quantum Machine Learning. 1034-1043 - Quasi-Interpolation. 1043
- Sanjay Jain, Frank Stephan:
Query-Based Learning. 1044-1047
R
- Radial Basis Function Approximation. 1049
- Martin D. Buhmann:
Radial Basis Function Networks. 1049-1054 - Radial Basis Function Neural Networks. 1054
- Random Decision Forests. 1054
- Random Forests. 1054
- Random Subspace Method. 1055
- Random Subspaces. 1055
- Randomized Decision Rule. 1055
- Randomized Experiments. 1055
- Johannes Fürnkranz, Eyke Hüllermeier:
Rank Correlation. 1055 - Ratio Scale. 1056
- Real-Time Dynamic Programming. 1056