Pattern Recognition: Introduction, Features, Classifiers and Principles

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Walter de Gruyter GmbH & Co KG, 04.12.2017 - 311 Seiten

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 is to 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.

  • Mathematical methods explained thoroughly
  • Extremely practical approach with many examples
  • Based on over ten years lecture at Karlsruhe Institute of Technology
  • For students but also for practitioners
 

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Inhalt

1 Fundamentals and definitions
1
2 Features
10
3 Bayesian decision theory
98
4 Parameter estimation
122
5 Parameter free methods
142
6 General considerations
162
7 Special classifiers
173
8 Classification with nominal features
215
9 Classifierindependent concepts
231
A Solutions to the exercises
249
B A primer on Lie theory
263
C Random processes
268
Bibliography
271
Glossary
275
Index
281
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Über den Autor (2017)

Jürgen Beyerer, Matthias Richter, Matthias Nagel, Fraunhofer Institute Karlsruhe, Germany.

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