Springer出版 Encyclopedia of Machine Learning

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the several hundred entries in this pre-eminent work include useful literature references, providing the reader with a portal to more detailed information on any given topic.

Table of contents Clustering.- Statistical Machine Learning.- Statistical Language Learning.- Inductive Logic Programming.- Learning and Logic.- Meta-Learning.- ROC analysis.- Information Theory.- Instance-based Learning Time Series.- Policy Search and Active Selection.- Reinforcement Learning.- Artificial Neural Network.- Text Mining.- Machine Learning in Bioinformatics.- Rule Learning.- Evolutionary Computation.- Behavioral Cloning.- Search.- Computational Learning Theory.- Online Learning.- Learning Paradigms.- Model-based Reinforcement Learning.- Active Learning.- Explanation-based Learning.- Data Mining.- Graph Mining