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Data Complexity in Pattern Recognition

By Mitra Basu , Tin Kam Ho

Data Complexity in Pattern Recognition Cover Image

  • ISBN13: 978-1-8462-8171-6
  • 320 Pages
  • User Level: Science
  • Publication Date: December 22, 2006
  • Available eBook Formats: PDF
  • eBook Price: $149.00
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Full Description
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.
Table of Contents

Table of Contents

  1. Theory and Methodology.
  2. Measures of Geometrical Complexity in Classification Problems.
  3. Object Representation, Sample Size and Dataset Complexity.
  4. Measures of Data and Classifier Complexity and the Training Sample Size.
  5. Linear Separability in Descent Procedures for Linear Classifiers.
  6. Data Complexity, Margin
  7. based Learning and Popper’s Philosophy of Inductive Learning.
  8. Data Complexity and Evolutionary Learning.
  9. Data Complexity and Domains of Competence of Classifiers.
  10. Data Complexity Issues in Grammatical Inference.
  11. Applications.
  12. Simple Statistics for Complex Feature Spaces.
  13. Polynomial Time for Complexity Graph Distance Computation for Web Content Mining.
  14. Data Complexity in Clustering Analysis for Gene Microarray Expression Profiles.
  15. Complexity of Magnetic Resonance Spectrum Classification.
  16. Data Complexity in Tropical Cyclone Positioning and Classification.
  17. Human
  18. Computer Interaction for Complex Pattern Recognition Problems.
  19. Complex Image Recognition and Web Security.
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