An introduction to machine learning

Research output: Book/ReportBook

44 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
PublisherSpringer International Publishing
Number of pages291
ISBN (Print)9783319200101, 9783319200095
DOIs
StatePublished - Jan 1 2015

Fingerprint

Learning systems
Classifiers
Decision trees
Support vector machines
Genetic algorithms
Polynomials
Students
Neural networks

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

An introduction to machine learning. / Kubat, Miroslav.

Springer International Publishing, 2015. 291 p.

Research output: Book/ReportBook

Kubat, Miroslav. / An introduction to machine learning. Springer International Publishing, 2015. 291 p.
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