© 2006

Data Complexity in Pattern Recognition

  • Mitra Basu
  • Tin Kam Ho
  • 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 Advanced Information and Knowledge Processing book series (AI&KP)

Table of contents

  1. Front Matter
    Pages I-XV
  2. Theory and Methodology

    1. Front Matter
      Pages 1-1
    2. Tin Kam Ho, Mitra Basu, Martin Hiu Chung Law
      Pages 1-23
    3. Robert P. W. Duin, Elżzbieta Pękalska
      Pages 25-58
    4. Ester Bernadó-Mansilla, Tin Kam Ho, Albert Orriols
      Pages 115-134
    5. Tin Kam Ho, Ester Bernadó-Mansilla
      Pages 135-152
    6. Colin de la Higuera
      Pages 153-169
  3. Applications

    1. Front Matter
      Pages 171-171
    2. George Nagy, Xiaoli Zhang
      Pages 173-195
    3. Adam Schenker, Horst Bunke, Mark Last, Abraham Kandel
      Pages 197-215
    4. Richard Baumgartner, Tin Kam Ho, Ray Somorjai, Uwe Himmelreich, Tania Sorrell
      Pages 241-248
    5. Chi Lap Yip, Ka Yan Wong, Ping Wah Li
      Pages 249-270
    6. Henry S. Baird
      Pages 287-298
  4. Back Matter
    Pages 299-300

About this book


Machines capable of automatic pattern recognition have many fascinating uses in science and 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. Tremendous progress has been made in refining such algorithms; yet, automatic learning in many simple tasks in daily life still appears to be far from reach.

This book takes a close view of data complexity and its role in shaping the theories and techniques in different disciplines and 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?

Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas.


algorithm algorithms classification clustering cognition complexity evolution graph human-computer interaction (HCI) knowledge learning neural networks pattern pattern recognition philosophy

Editors and affiliations

  • Mitra Basu
    • 1
  • Tin Kam Ho
    • 2
  1. 1.Electrical Engineering DepartmentCity College, City University of New YorkUSA
  2. 2.Bell Laboratories, Lucent TechnologiesUSA

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