Data Complexity in Pattern Recognition

Data Complexity in Pattern Recognition edited by Mitra Basu, Tin Kam Ho.

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 progre...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Basu, Mitra (Editor), Ho, Tin Kam (Editor)
Format: eBook
Language:English
Published: London : Springer London : Imprint: Springer, 2006.
Edition:1st ed. 2006.
Series:Advanced Information and Knowledge Processing,
Springer eBook Collection.
Subjects:
Pattern recognition.
Artificial intelligence.
Algorithms.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Theory and Methodology
  • Measures of Geometrical Complexity in Classification Problems
  • Object Representation, Sample Size, and Data Set Complexity
  • Measures of Data and Classifier Complexity and the Training Sample Size
  • Linear Separability in Descent Procedures for Linear Classifiers
  • Data Complexity, Margin-Based Learning, and Popper’s Philosophy of Inductive Learning
  • Data Complexity and Evolutionary Learning
  • Classifier Domains of Competence in Data Complexity Space
  • Data Complexity Issues in Grammatical Inference
  • Applications
  • Simple Statistics for Complex Feature Spaces
  • Polynomial Time Complexity Graph Distance Computation for Web Content Mining
  • Data Complexity in Clustering Analysis of Gene Microarray Expression Profiles
  • Complexity of Magnetic Resonance Spectrum Classification
  • Data Complexity in Tropical Cyclone Positioning and Classification
  • Human-Computer Interaction for Complex Pattern Recognition Problems
  • Complex Image Recognition and Web Security.

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