Statistical and Neural Classifiers

An Integrated Approach to Design

  • Šarūnas Raudys

Part of the Advances in Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Šarūnas Raudys
    Pages 1-26
  3. Šarūnas Raudys
    Pages 27-75
  4. Šarūnas Raudys
    Pages 77-134
  5. Šarūnas Raudys
    Pages 135-190
  6. Šarūnas Raudys
    Pages 191-207
  7. Šarūnas Raudys
    Pages 209-266
  8. Back Matter
    Pages 267-289

About this book


Automatic (machine) recognition, description, classification, and groupings of patterns are important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. Given a pattern, its recognition/classification may consist of one of the following two tasks: (1) supervised classification (also called discriminant analysis); the input pattern is assigned to one of several predefined classes, (2) unsupervised classification (also called clustering); no pattern classes are defined a priori and patterns are grouped into clusters based on their similarity. Interest in the area of pattern recognition has been renewed recently due to emerging applications which are not only challenging but also computationally more demanding (e. g. , bioinformatics, data mining, document classification, and multimedia database retrieval). Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have received increased attention. Neural networks and statistical pattern recognition are two closely related disciplines which share several common research issues. Neural networks have not only provided a variety of novel or supplementary approaches for pattern recognition tasks, but have also offered architectures on which many well-known statistical pattern recognition algorithms can be mapped for efficient (hardware) implementation. On the other hand, neural networks can derive benefit from some well-known results in statistical pattern recognition.


Excel Image Processing MATLAB Maxima Multimedia Neural Networks Pattern Recognition Performance Speech Processing algorithms complexity data analysis decision theory robot speech recognition

Authors and affiliations

  • Šarūnas Raudys
    • 1
  1. 1.Data Analysis DepartmentInstitute of Mathematics and InformaticsVilniusLithuania

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London 2001
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-85233-297-6
  • Online ISBN 978-1-4471-0359-2
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • Buy this book on publisher's site