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Ten Lectures on Statistical and Structural Pattern Recognition

  • Book
  • © 2002

Overview

Authors:
  1. Michail I. Schlesinger

    1. Ukranian Academy of Sciences, Kiev, Ukraine

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    You can also search for this author in PubMed   Google Scholar

  2. Václav Hlaváč

    1. Czech Technical University, Prague, Czech Republic

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    You can also search for this author in PubMed   Google Scholar

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

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xix
    Download chapter PDF

  2. Bayesian statistical decision making

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 1-23

  3. Non-Bayesian statistical decision making

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 25-71

  4. Two statistical models of the recognised object

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 73-99

  5. Learning in pattern recognition

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 101-136

  6. Linear discriminant function

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 137-214

  7. Unsupervised learning

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 215-274

  8. Mutual relationship of statistical and structural recognition

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 275-305

  9. Recognition of Markovian sequences

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 307-395

  10. Regular languages and corresponding pattern recognition tasks

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 397-477

  11. Context-free languages, their two-dimensional generalisation, related tasks

    • Michail I. Schlesinger, Václav Hlaváč
    Pages 479-505

  12. Back Matter

    Pages 507-522
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Keywords

About this book

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.

Authors and Affiliations

  • Ukranian Academy of Sciences, Kiev, Ukraine

    Michail I. Schlesinger

  • Czech Technical University, Prague, Czech Republic

    Václav Hlaváč

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