Table of contents

  1. Front Matter
    Pages i-xiii
  2. Miroslav Kubat
    Pages 1-18
  3. Miroslav Kubat
    Pages 19-41
  4. Miroslav Kubat
    Pages 43-64
  5. Miroslav Kubat
    Pages 91-111
  6. Miroslav Kubat
    Pages 113-135
  7. Miroslav Kubat
    Pages 137-150
  8. Miroslav Kubat
    Pages 151-171
  9. Miroslav Kubat
    Pages 173-189
  10. Miroslav Kubat
    Pages 191-210
  11. Miroslav Kubat
    Pages 211-229
  12. Miroslav Kubat
    Pages 231-249
  13. Miroslav Kubat
    Pages 251-271
  14. Miroslav Kubat
    Pages 273-295
  15. Miroslav Kubat
    Pages 297-308
  16. Miroslav Kubat
    Pages 309-329
  17. Miroslav Kubat
    Pages 331-339
  18. Back Matter
    Pages 341-348

About this book


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.


Bayesian classifiers boosting computational learning theory decision trees genetic algorithms linear and polynomial classifiers nearest neighbor classifier neural networks performance evaluation reinforcement learning statistical learning time-varying classes, imbalanced representation artificial intelligence machine learning data mining deep learning unsupervised learning

Authors and affiliations

  • Miroslav Kubat
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of MiamiCoral GablesUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-63912-3
  • Online ISBN 978-3-319-63913-0
  • Buy this book on publisher's site