Data Complexity in Pattern Recognition

By: Mitra Basu (Editor), Tin Kam Ho (Editor)

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Hardcover | 1 September 2006

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320 Pages

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24.13 x 16.51 x 1.91

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Classification & data characteristics are important issues that can be found in many disciplines. A good understanding of the complexity of a specific data set arising from an application is a prerequisite for setting a proper expectation on classification performance, & to addressing questions like whether, or to what extent, patterns exist in a specific problem. It can also provide an important guide to the selection of classification techniques. However, there is a huge & diverse literature in this field, which can make it very difficult to keep up with latest advances. Classification is a central topic in several disciplines related to pattern recognition including machine learning, neural networks, & data mining, & it has numerous practical applications in image, video & speech signal processing, biometrics, digital libraries etc. Differences in approach and terminology often mean that different communities are unaware of concurrent developments under slightly difference names.This book is unique in its complete coverage & multidisciplinary approach. Classification & data characteristics are important issues that are all pervasive & impact upon real-world problems - researchers & practitioners alike will find this book an excellent reference source.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Computer Vision Pattern Recognition Computer Programming & Software Development Algorithms & Data Structures

List of Contributors	p. XI
Theory and Methodology
Measures of Geometrical Complexity in Classification Problems	p. 3
Object Representation, Sample Size, and Data Set Complexity	p. 25
Measures of Data and Classifier Complexity and the Training Sample Size	p. 59
Linear Separability in Descent Procedures for Linear Classifiers	p. 69
Data Complexity, Margin-Based Learning, and Popper's Philosophy of Inductive Learning	p. 91
Data Complexity and Evolutionary Learning	p. 115
Classifier Domains of Competence in Data Complexity Space	p. 135
Data Complexity Issues in Grammatical Inference	p. 153
Applications
Simple Statistics for Complex Feature Spaces	p. 173
Polynomial Time Complexity Graph Distance Computation for Web Content Mining	p. 197
Data Complexity in Clustering Analysis of Gene Microarray Expression Profiles	p. 217
Complexity of Magnetic Resonance Spectrum Classification	p. 241
Data Complexity in Tropical Cyclone Positioning and Classification	p. 249
Human-Computer Interaction for Complex Pattern Recognition Problems	p. 271
Complex Image Recognition and Web Security	p. 287
Index	p. 299
Table of Contents provided by Ingram. All Rights Reserved.

Data Complexity in Pattern Recognition

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Hardcover

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