Ten Lectures on Statistical and Structural Pattern Recognition

  • Michail I. Schlesinger
  • Václav Hlaváč

Part of the Computational Imaging and Vision book series (CIVI, volume 24)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Michail I. Schlesinger, Václav Hlaváč
    Pages 1-23
  3. Michail I. Schlesinger, Václav Hlaváč
    Pages 25-71
  4. Michail I. Schlesinger, Václav Hlaváč
    Pages 73-99
  5. Michail I. Schlesinger, Václav Hlaváč
    Pages 101-136
  6. Michail I. Schlesinger, Václav Hlaváč
    Pages 137-214
  7. Michail I. Schlesinger, Václav Hlaváč
    Pages 215-274
  8. Michail I. Schlesinger, Václav Hlaváč
    Pages 275-305
  9. Michail I. Schlesinger, Václav Hlaváč
    Pages 307-395
  10. Michail I. Schlesinger, Václav Hlaváč
    Pages 397-477
  11. Michail I. Schlesinger, Václav Hlaváč
    Pages 479-505
  12. Back Matter
    Pages 507-522

About this book

Introduction

Preface to the English edition This monograph Ten Lectur,es on Statistical and Structural Pattern Recognition uncovers the close relationship between various well known pattern recognition problems that have so far been considered independent. These relationships became apparent when formal procedures addressing not only known prob­ lems but also their generalisations were discovered. The generalised problem formulations were analysed mathematically and unified algorithms were found. The book unifies of two main streams ill pattern recognition-the statisti­ cal a11d structural ones. In addition to this bridging on the uppermost level, the book mentions several other unexpected relations within statistical and structural methods. The monograph is intended for experts, for students, as well as for those who want to enter the field of pattern recognition. The theory is built up from scratch with almost no assumptions about any prior knowledge of the reader. Even when rigorous mathematical language is used we make an effort to keep the text easy to comprehend. This approach makes the book suitable for students at the beginning of their scientific career. Basic building blocks are explained in a style of an accessible intellectual exercise, thus promoting good practice in reading mathematical text. The paradoxes, beauty, and pitfalls of scientific research are shown on examples from pattern recognition. Each lecture is amended by a discussion with an inquisitive student that elucidates and deepens the explanation, providing additional pointers to computational procedures and deep rooted errors.

Keywords

algorithms cognition learning pattern recognition supervised learning

Authors and affiliations

  • Michail I. Schlesinger
    • 1
  • Václav Hlaváč
    • 2
  1. 1.Ukranian Academy of SciencesKievUkraine
  2. 2.Czech Technical UniversityPragueCzech Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-017-3217-8
  • Copyright Information Springer Science+Business Media B.V. 2002
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-481-6027-3
  • Online ISBN 978-94-017-3217-8
  • Series Print ISSN 1381-6446
  • About this book
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