Encyclopedia of Machine Learning 2010

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