Pattern Recognition

Introduction, Features, Classifiers and Principles
Buch | Softcover
XXVIII, 283 Seiten
2017
De Gruyter Oldenbourg (Verlag)
978-3-11-053793-2 (ISBN)

Lese- und Medienproben

Pattern Recognition - Jürgen Beyerer, Matthias Richter, Matthias Nagel
84,95 inkl. MwSt
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 thoroughlyExtremely practical approach with many examplesBased on over ten years lecture at Karlsruhe Institute of TechnologyFor students but also for practitioners

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

Erscheinungsdatum
Reihe/Serie De Gruyter Textbook
Zusatzinfo 200 col. ill., 4 b/w tbl.
Verlagsort Basel/Berlin/Boston
Sprache englisch
Maße 170 x 240 mm
Gewicht 585 g
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik Elektrotechnik / Energietechnik
Schlagworte Artificial Intelligence • Automation • Data Mining • machine learning • Textbook
ISBN-10 3-11-053793-1 / 3110537931
ISBN-13 978-3-11-053793-2 / 9783110537932
Zustand Neuware
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