Studies in Computational Intelligence

Non-Standard Parameter Adaptation for Exploratory Data Analysis

Authors: Barbakh, Wesam Ashour, Wu, Ying, Fyfe, Colin

  • Presents novel methods of parameter adaptation in machine learning
  • Acts as a valuable contribution to create a true artificial intelligence
  • Recent research in reinforcement learning, cross entropy and artificial immune systems for exploratory data analysis
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eBook $159.00
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  • ISBN 978-3-642-04005-4
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Hardcover $179.99
price for USA in USD
  • ISBN 978-3-642-04004-7
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  • Usually dispatched within 3 to 5 business days.
Softcover $209.00
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  • ISBN 978-3-642-26055-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets.

We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods.

We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation.

Table of contents (11 chapters)

  • Introduction

    Barbakh, Wesam Ashour (et al.)

    Pages 1-6

  • Review of Clustering Algorithms

    Barbakh, Wesam Ashour (et al.)

    Pages 7-28

  • Review of Linear Projection Methods

    Barbakh, Wesam Ashour (et al.)

    Pages 29-48

  • Non-standard Clustering Criteria

    Barbakh, Wesam Ashour (et al.)

    Pages 49-72

  • Topographic Mappings and Kernel Clustering

    Barbakh, Wesam Ashour (et al.)

    Pages 73-84

Buy this book

eBook $159.00
price for USA in USD (gross)
  • ISBN 978-3-642-04005-4
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $179.99
price for USA in USD
  • ISBN 978-3-642-04004-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $209.00
price for USA in USD
  • ISBN 978-3-642-26055-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Non-Standard Parameter Adaptation for Exploratory Data Analysis
Authors
Series Title
Studies in Computational Intelligence
Series Volume
249
Copyright
2009
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-04005-4
DOI
10.1007/978-3-642-04005-4
Hardcover ISBN
978-3-642-04004-7
Softcover ISBN
978-3-642-26055-1
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XI, 223
Topics