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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