Encyclopedia of Machine Learning and Data Mining

Claude Sammut is a Professor of Computer Science and Engineering at the University of New South Wales, Australia, and Head of the Artificial Intelligence Research Group. He is the UNSW node Director of the ARC Centre of Excellence for Autonomous Systems and a member of the joint ARC/NH&MRC project on Thinking Systems. He is on the editorial boards of the Journal of Machine Learning Research, the Machine Learning Journal and New Generation Computing, and was the chairman of the 2007 International Conference on Machine Learning.Geoffrey I. Webb is research professor in the faculty of Information Technology at Monash University, Melbourne, Australia. He has published more than 150 scientific papers and is the author of the data mining software package Magnum Opus. His research areas include strategies for strengthening the Naïve Bayes machine learning technique, K-optimal pattern discovery, and work on Occam’s razor. He is editor-in-chief of Springer’s Data Mining and Knowledge Discovery journal, as well as being on the editorial board of Machine Learning.

• 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.

“The topics covered in the revised edition include applications, data mining, evolutionary computation, graph mining, information theory, learning and logic, pattern mining, reinforcement learning, relational mining, statistical learning, and text mining. … I recommend the encyclopedia as a valuable resource for libraries … .” (S. V. Nagaraj, Computing Reviews, January, 2018)​