A probabilistic theory of pattern recognition /
Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance me...
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
New York :
Springer,
[1996]
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| Series: | Applications of mathematics ;
31. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Introduction
- The Bayes Error
- Inequalities and alternate distance measures
- Linear discrimination
- Nearest neighbor rules
- Consistency
- Slow rates of convergence
- Error estimation
- The regular histogram rule
- Kernel rules
- Consistency of the k-nearest neighbor rule
- Vapnik-Chervonenkis theory
- Combinatorial aspects of Vapnik-Chervonenkis theory
- Lower bounds for empirical classifier selection
- The maximum likelihood principle
- Parametric classification
- Generalized linear discrimination
- Complexity regularization
- Condensed and edited nearest neighbor rules
- Tree classifiers
- Data-dependent partitioning
- Splitting the data
- The resubstitution estimate
- Deleted estimates of the error probability
- Automatic kernel rules
- Automatic nearest neighbor rules
- Hypercubes and discrete spaces
- Epsilon entropy and totally bounded sets
- Uniform laws of large numbers
- Neural networks
- Other error estimates
- Feature extraction.