Encyclopedia of Machine Learning and Data Mining

Encyclopedia of Machine Learning and Data Mining

Hardcover(2nd ed. 2017)

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Overview

Encyclopedia of Machine Learning and Data Mining by Claude Sammut


This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.
Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.
The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.

Product Details

ISBN-13: 9781489976857
Publisher: Springer US
Publication date: 03/15/2017
Edition description: 2nd ed. 2017
Pages: 1335
Product dimensions: 7.01(w) x 10.00(h) x (d)

About the Author

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.

Table of Contents

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.

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