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Data Complexity in Pattern Recognition

  • Book
  • © 2006

Overview

Editors:
  1. Mitra Basu

    1. Electrical Engineering Department, City College, City University of New York, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Tin Kam Ho

    1. Bell Laboratories, Lucent Technologies, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Shows how to appreciate the presence and nature of patterns in specific problems
  • Helps the reader set proper expectations for classification performance
  • Offers guidance on choosing the best pattern recognition classification techniques
  • Interdisciplinary coverage helps the reader absorb and apply useful developments in diverse fields: Engineering, Computer Science, Social Sciences and Finance

Part of the book series: Advanced Information and Knowledge Processing (AI&KP)

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Table of contents (15 chapters)

  1. Front Matter

    Pages I-XV
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  2. Theory and Methodology

    1. Front Matter

      Pages 1-1
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    2. Measures of Geometrical Complexity in Classification Problems

      • Tin Kam Ho, Mitra Basu, Martin Hiu Chung Law
      Pages 1-23

    3. Object Representation, Sample Size, and Data Set Complexity

      • Robert P. W. Duin, Elżzbieta PÄ™kalska
      Pages 25-58

    4. Measures of Data and Classifier Complexity and the Training Sample Size

      • Å arÅ«nas Raudys
      Pages 60-68

    5. Linear Separability in Descent Procedures for Linear Classifiers

      • Mitra Basu, Tin Kam Ho
      Pages 69-90

    6. Data Complexity, Margin-Based Learning, and Popper’s Philosophy of Inductive Learning

      • Vladimir Cherkassky, Yunqian Ma
      Pages 91-114

    7. Data Complexity and Evolutionary Learning

      • Ester Bernadó-Mansilla, Tin Kam Ho, Albert Orriols
      Pages 115-134

    8. Classifier Domains of Competence in Data Complexity Space

      • Tin Kam Ho, Ester Bernadó-Mansilla
      Pages 135-152

    9. Data Complexity Issues in Grammatical Inference

      • Colin de la Higuera
      Pages 153-169

  3. Applications

    1. Front Matter

      Pages 171-171
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    2. Simple Statistics for Complex Feature Spaces

      • George Nagy, Xiaoli Zhang
      Pages 173-195

    3. Polynomial Time Complexity Graph Distance Computation for Web Content Mining

      • Adam Schenker, Horst Bunke, Mark Last, Abraham Kandel
      Pages 197-215

    4. Data Complexity in Clustering Analysis of Gene Microarray Expression Profiles

      • Feng Luo, Latifur Khan
      Pages 217-239

    5. Complexity of Magnetic Resonance Spectrum Classification

      • Richard Baumgartner, Tin Kam Ho, Ray Somorjai, Uwe Himmelreich, Tania Sorrell
      Pages 241-248

    6. Data Complexity in Tropical Cyclone Positioning and Classification

      • Chi Lap Yip, Ka Yan Wong, Ping Wah Li
      Pages 249-270

    7. Human-Computer Interaction for Complex Pattern Recognition Problems

      • Jie Zou, George Nagy
      Pages 271-286

    8. Complex Image Recognition and Web Security

      • Henry S. Baird
      Pages 287-298

  4. Back Matter

    Pages 299-300
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Keywords

About this book

Machines capable of automatic pattern recognition have many fascinating uses in science & engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability.

This book takes a close view of data complexity & its role in shaping the theories & techniques in different disciplines & asks:

  • What is missing from current classification techniques?
  • When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task?
  • How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data?

Uunique in its comprehensive coverage & multidisciplinary approach from various methodological & practical perspectives, researchers & practitioners will find this book an insightful reference to learn about current available techniques as well as application areas.

Editors and Affiliations

  • Electrical Engineering Department, City College, City University of New York, USA

    Mitra Basu

  • Bell Laboratories, Lucent Technologies, USA

    Tin Kam Ho

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