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Causal Models and Intelligent Data Management

  • Alex Gammerman

Table of contents

  1. Front Matter
    Pages I-X
  2. Casual Models

    1. Front Matter
      Pages 1-1
    2. J. Pearl
      Pages 3-16
    3. Glenn Shafer
      Pages 17-32
    4. A. P. Dawid
      Pages 33-50
    5. Nancy Cartwright
      Pages 51-63
  3. Intelligent Data Management

    1. Front Matter
      Pages 65-65
    2. A. Gammerman, V. Vovk
      Pages 81-88
    3. Chris S. Wallace, Kevin B. Korb
      Pages 89-111
    4. Y. E. Malashenko, N. M. Novikova, O. A. Vorobeichikova
      Pages 112-119
    5. Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
      Pages 120-136
    6. Xiaohui Liu
      Pages 137-150
    7. Richard S. Forsyth
      Pages 151-185

About this book

Introduction

Data analysis and inference have traditionally been research areas of statistics. However, the need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new methods and tools, new types of databases, new efficient algorithms, new data structures, etc. - in effect new computational methods.
This monograph presents new intelligent data management methods and tools, such as the support vector machine, and new results from the field of inference, in particular of causal modeling. In 11 well-structured chapters, leading experts map out the major tendencies and future directions of intelligent data analysis. The book will become a valuable source of reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry and commerce. Students and lecturers will find the book useful as an introduction to the area.

Keywords

Apple Support Vector Machine Syntax algorithms classification cognition complexity computer science data analysis databases evolutionary algorithm learning machine learning modeling pattern recognition

Editors and affiliations

  • Alex Gammerman
    • 1
  1. 1.Department of Computer ScienceUniversity of LondonRoyal HollowayUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-58648-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 1999
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-63682-0
  • Online ISBN 978-3-642-58648-4
  • Buy this book on publisher's site
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