An Introduction to Machine Learning / by Miroslav Kubat.

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An Introduction to Machine Learning / by Miroslav Kubat.

2nd ed. 2017.

9783319639130 electronic book
3319639137 electronic book
9783319639123
3319639129

Cham : Springer International Publishing : Imprint: Springer, 2017.

English

1 online resource (xiii, 348 pages) : illustrations.

10.1007/978-3-319-63913-0 doi

QA76.9.D343

006.3/1

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of "boosting," how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.

Includes bibliographical references and index.

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Print version: 9783319639123

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