- (Sammut & Webb, 2017) ⇒ Claude Sammut, and Geoffrey I. Webb. (2017). “Encyclopedia of Machine Learning and Data Mining.” Springer. ISBN: 978-1-4899-7685-7
Subject Headings: Machine Learning Encyclopedia.
- It is a followup to (Sammut & Webb, 2011) ⇒ Claude Sammut (editor), and Geoffrey I. Webb (editor). (2011). “Encyclopedia of Machine Learning." Springer. ISBN:0387307680
- It can be found online here https://link.springer.com/referencework/10.1007/978-1-4899-7502-7
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
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.
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