Studies in Computational Intelligence

Multi-Objective Machine Learning

Editors: Jin, Yaochu (Ed.)

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About this book

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.

Table of contents (1 chapters)

  • Feature Selection for Ensembles Using the Multi-Objective Optimization Approach

    Luiz S. Oliveira, Marisa Morita, Robert Sabourin

    Pages

Buy this book

eBook $219.00
price for USA (gross)
  • ISBN 978-3-540-33019-6
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $279.99
price for USA
  • ISBN 978-3-540-30676-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $279.99
price for USA
  • ISBN 978-3-642-06796-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Multi-Objective Machine Learning
Editors
  • Yaochu Jin
Series Title
Studies in Computational Intelligence
Series Volume
16
Copyright
2006
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-33019-6
DOI
10.1007/3-540-33019-4
Hardcover ISBN
978-3-540-30676-4
Softcover ISBN
978-3-642-06796-9
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XIV, 660
Number of Illustrations and Tables
254 b/w illustrations
Topics