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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