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An Introduction to Machine Learning

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This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting 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.

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Detail Information

Series Title

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Call Number

006.3 KUB i

Publisher

Cham, Switzerland : Springer Cham., 2015

Collation

XIII, 291

Language

English

ISBN/ISSN

978-3-319-20010-1

Classification

006.3

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

Ed. 1

Subject(s)

Artificial Intelligence
Computer Modelling

Specific Detail Info

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Statement of Responsibility

Miroslav Kubat

Other Information

Cataloger

umi

Source

https://link.springer.com/content/pdf/10.1007/978-3-319-20010-1.pdf?pdf=button

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