Pattern Recognition
eBook - ePub

Pattern Recognition

Introduction, Features, Classifiers and Principles

  1. 348 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Pattern Recognition

Introduction, Features, Classifiers and Principles

About this book

The book offers a thorough introduction to Pattern Recognition aimed at master and advanced bachelor students of engineering and the natural sciences. Besides classification - the heart of Pattern Recognition - special emphasis is put on features: their typology, their properties and their systematic construction. Additionally, general principles that govern Pattern Recognition are illustrated and explained in a comprehensible way. Rather than presenting a complete overview over the rapidly evolving field, the book clarifies the concepts so that the reader can easily understand the underlying ideas and the rationale behind the methods. For this purpose, the mathematical treatment of Pattern Recognition is pushed so far that the mechanisms of action become clear and visible, but not farther. Therefore, not all derivations are driven into the last mathematical detail, as a mathematician would expect it. Ideas of proofs are presented instead of complete proofs. From the authors' point of view, this concept allows to teach the essential ideas of Pattern Recognition with sufficient depth within a relatively lean book.

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Yes, you can access Pattern Recognition by Jürgen Beyerer,Raphael Hagmanns,Daniel Stadler in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Processing. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Title Page
  2. Copyright
  3. Contents
  4. Preface
  5. Preface of 2nd edition
  6. List of Tables
  7. List of Figures
  8. Notation
  9. Introduction
  10. 1 Fundamentals and definitions
  11. 2 Features
  12. 3 Bayesian decision theory
  13. 4 Parameter estimation
  14. 5 Parameter free methods
  15. 6 General considerations
  16. 7 Special classifiers
  17. 8 Classification with nominal features
  18. 9 Classifier-independent concepts
  19. A Solutions to the exercises
  20. B A primer on Lie theory
  21. C Random processes
  22. Glossary
  23. Index