Encyclopedia of Machine Learning and Data Mining

Abduction.-  Adaptive Resonance Theory.-  Anomaly Detection.-  Bayes Rule.-  Case-Based Reasoning.-  Categorical Data Clustering.-  Causality.-  Clustering from Data Streams.-  Complexity in Adaptive Systems.-  Complexity of Inductive Inference.-  Computational Complexity of Learning.-  Confusion Matrix.-  Connections Between Inductive Inference and Machine Learning.-  Covariance Matrix.-  Decision List.-  Decision Lists and Decision Trees.-  Decision Tree.-  Deep Learning.-  Density-Based Clustering.-  Dimensionality Reduction.-  Document Classification.-  Dynamic Memory Model.-  Empirical Risk Minimization.-  Error Rate.-  Event Extraction from Media Texts.-  Evolutionary Clustering.-  Evolutionary Computation in Economics.-  Evolutionary Computation in Finance.-  Evolutionary Computational Techniques in Marketing.-  Evolutionary Feature Selection and Construction.-  Evolutionary Kernel Learning.-  Evolutionary Robotics.-  Expectation Maximization Clustering.-  Expectation Propagation.-  Feature Construction in Text Mining.-  Feature Selection.-  Feature Selection in Text Mining.-  Gaussian Distribution.-  Gaussian Process.-  Generative and Discriminative Learning.-  Grammatical Inference.-  Graphical Models.-  Hidden Markov Models.-  Inductive Inference.-  Inductive Logic Programming.-  Inductive Programming.-  Inductive Transfer.-  Inverse Reinforcement Learning.-  Kernel Methods.-  K-Means Clustering.-  K-Medoids Clustering.-  K-Way Spectral Clustering.-  Learning Algorithm Evaluation.-  Learning Graphical Models.-  Learning Models of Biological Sequences.-  Learning to Rank.-  Learning Using Privileged Information.-  Linear Discriminant.-  Linear Regression.-  Locally Weighted Regression for Control.-  Machine Learning and Game Playing.-  Manhattan Distance.-  Maximum Entropy Models for Natural Language Processing.-  Mean Shift.-  Metalearning.-  Minimum Description Length Principle.-  Minimum Message Length.-  Mixture Model.-  Model Evaluation.-  Model Trees.-  Multi Label Learning.-  Naïve Bayes.-  Occam's Razor.-  Online Controlled Experiments and A/B Testing.-  Online Learning.-  Opinion Stream Mining .-  PAC Learning.-  Partitional Clustering.-  Phase Transitions in Machine Learning.