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Semantic ScholarEncyclopedia 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. 3-9Absolute Error Loss. 9 Accuracy. 9-10 ACO. 10 Actions. 10 David Cohn:
Active Learning. 10-14Sanjoy Dasgupta:
Active Learning Theory. 14-19Adaboost. 19 Adaptive Control Processes. 19 Andrew G. Barto:
Adaptive Real-Time Dynamic Programming. 19-22Gail A. Carpenter, Stephen Grossberg:
Adaptive Resonance Theory. 22-35Adaptive 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. 35-36Analogical Reasoning. 36 Analysis of Text. 36 Analytical Learning. 36 Marco Dorigo, Mauro Birattari:
Ant Colony Optimization. 36-39Anytime Algorithm. 39 AODE. 39 Apprenticeship Learning. 39 Approximate Dynamic Programming. 39 Hannu Toivonen:
Apriori Algorithm. 39-40AQ. 40 Area Under Curve. 40 ARL. 40 ART. 40 ARTDP. 40 Jon Timmis:
Artificial Immune Systems. 40-44Artificial Life. 44 Artificial Neural Networks. 44 Jürgen Branke:
Artificial Societies. 44-48Assertion. 48 Hannu Toivonen:
Association Rule. 48-49Associative Bandit Problem. 49 Alexander L. Strehl:
Associative Reinforcement Learning. 49-51Chris Drummond:
Attribute. 51-53Attribute Selection. 53 Attribute-Value Learning. 53 AUC. 53 Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Autonomous Helicopter Flight Using Reinforcement Learning. 53-61Average-Cost Neuro-Dynamic Programming. 63 Average-Cost Optimization. 63 Fei Zheng, Geoffrey I. Webb:
Averaged One-Dependence Estimators. 63-64Average-Payoff Reinforcement Learning. 64 Prasad Tadepalli:
Average-Reward Reinforcement Learning. 64-68
B
Backprop. 69-73 Paul W. Munro:
Backpropagation. 73Bagging. 73 Bake-Off. 73 Bandit Problem with Side Information. 73 Bandit Problem with Side Observations. 73 Basic Lemma. 73 Hannu Toivonen:
Basket Analysis. 74Batch Learning. 74 Baum-Welch Algorithm. 74 Bayes Adaptive Markov Decision Processes. 74 Bayes Net. 74 Geoffrey I. Webb:
Bayes Rule. 74-75Wray L. Buntine:
Bayesian Methods. 75-81Bayesian Model Averaging. 81 Bayesian Network. 81 Peter Orbanz, Yee Whye Teh:
Bayesian Nonparametric Models. 81-89Pascal Poupart:
Bayesian Reinforcement Learning. 90-93Claude Sammut:
Beam Search. 93Claude Sammut:
Behavioral Cloning. 93-97Belief State Markov Decision Processes. 97 Bellman Equation. 97 Bias. 97 Hendrik Blockeel:
Bias Specification Language. 98-100Bias Variance Decomposition. 100-101 Dev G. Rajnarayan, David H. Wolpert:
Bias-Variance Trade-offs: Novel Applications. 101-110Bias-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. 111-132C. David Page Jr., Sriraam Natarajan:
Biomedical Informatics. 132Blog Mining. 132 Geoffrey E. Hinton:
Boltzmann Machines. 132-136Boosting. 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. 139-147Cascor. 147 Case. 147 Case-Based Learning. 147 Susan Craw:
Case-Based Reasoning. 147-154Categorical Attribute. 154 Periklis Andritsos, Panayiotis Tsaparas:
Categorical Data Clustering. 154-159Categorization. 159 Category. 159 Causal Discovery. 159 Ricardo Bezerra de Andrade e Silva:
Causality. 159-166CBR. 166 CC. 166 Certainty Equivalence Principle. 166 Characteristic. 166 City Block Distance. 166 Chris Drummond:
Class. 166-171Charles X. Ling, Victor S. Sheng:
Class Imbalance Problem. 171Chris Drummond:
Classification. 171Classification Algorithms. 171 Classification Learning. 171 Classification Tree. 171 Pier Luca Lanzi:
Classifier Systems. 172-178Clause. 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. 180-183Clustering 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. 184-189Collaborative Filtering. 189 Collection. 189 Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor:
Collective Classification. 189-193Commercial 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. 194-198Sanjay Jain, Frank Stephan:
Complexity of Inductive Inference. 198-201Compositional Coevolution. 201 Sanjay Jain, Frank Stephan:
Computational Complexity of Learning. 201-202Computational Discovery of Quantitative Laws. 202 Claude Sammut, Michael Bonnell Harries:
Concept Drift. 202-205Claude Sammut:
Concept Learning. 205-208Conditional Random Field. 208 Confirmation Theory. 209 Kai Ming Ting:
Confusion Matrix. 209Bernhard Pfahringer:
Conjunctive Normal Form. 209-210Connection Strength. 210 John Case, Sanjay Jain:
Connections Between Inductive Inference and Machine Learning. 210-219Connectivity. 219 Consensus Clustering. 219-220 Kiri L. Wagstaff:
Constrained Clustering. 220-221Siegfried Nijssen:
Constraint-Based Mining. 221-225Constructive Induction. 225 Content Match. 226 Content-Based Filtering. 226 Content-Based Recommending. 226 Context-Sensitive Learning. 226