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Pattern recognition: introduction, features, classifiers and principles

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 systemati...

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Main Authors:	Beyerer, Jürgen 1961- (Author), Hagmanns, Raphael (Author), Stadler, Daniel (Author)
Format:	Electronic eBook
Language:	English
Published:	Berlin ; Boston De Gruyter Oldenbourg [2024]
Edition:	2nd edition
Series:	De Gruyter Textbook
Subjects:	Merkmalsextraktion Künstliche Intelligenz Data Mining Automatische Klassifikation Mustererkennung Maschinelles Lernen Automation
Links:	https://doi.org/10.1515/9783111339207 https://doi.org/10.1515/9783111339207 https://doi.org/10.1515/9783111339207 https://doi.org/10.1515/9783111339207 https://doi.org/10.1515/9783111339207 https://doi.org/10.1515/9783111339207 https://doi.org/10.1515/9783111339207 https://www.degruyter.com/document/isbn/9783111339207/html https://www.degruyter.com/document/isbn/9783111339207/html https://doi.org/10.1515/9783111339207 https://doi.org/10.1515/9783111339207 https://doi.org/10.1515/9783111339207 https://doi.org/10.1515/9783111339207
Summary:	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
Physical Description:	1 Online-Ressource (XXV, 327 Seiten)
ISBN:	9783111339207 9783111339412
DOI:	10.1515/9783111339207

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physical	1 Online-Ressource (XXV, 327 Seiten)
psigel	ZDB-23-DGG ZDB-23-DEI ZDB-23-OTI ZDB-23-DGG FAB_PDA_DGG ZDB-23-DGG FAW_PDA_DGG ZDB-23-DGG FCO_PDA_DGG ZDB-23-DEI ZDB-23-DEI24 ZDB-23-DGG FKE_PDA_DGG ZDB-23-DGG FLA_PDA_DGG ZDB-23-DEI TUM_Paketkauf_2024 ZDB-23-DGG UPA_PDA_DGG
publishDate	2024
publishDateSearch	2024
publishDateSort	2024
publisher	De Gruyter Oldenbourg
record_format	marc
series2	De Gruyter Textbook
spellingShingle	Beyerer, Jürgen 1961- Hagmanns, Raphael Stadler, Daniel Pattern recognition introduction, features, classifiers and principles Merkmalsextraktion (DE-588)4314440-8 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Data Mining (DE-588)4428654-5 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Mustererkennung (DE-588)4040936-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Automation (DE-588)4003957-2 gnd
subject_GND	(DE-588)4314440-8 (DE-588)4033447-8 (DE-588)4428654-5 (DE-588)4120957-6 (DE-588)4040936-3 (DE-588)4193754-5 (DE-588)4003957-2
title	Pattern recognition introduction, features, classifiers and principles
title_auth	Pattern recognition introduction, features, classifiers and principles
title_exact_search	Pattern recognition introduction, features, classifiers and principles
title_full	Pattern recognition introduction, features, classifiers and principles Jürgen Beyerer, Raphael Hagmanns, Daniel Stadler
title_fullStr	Pattern recognition introduction, features, classifiers and principles Jürgen Beyerer, Raphael Hagmanns, Daniel Stadler
title_full_unstemmed	Pattern recognition introduction, features, classifiers and principles Jürgen Beyerer, Raphael Hagmanns, Daniel Stadler
title_short	Pattern recognition
title_sort	pattern recognition introduction features classifiers and principles
title_sub	introduction, features, classifiers and principles
topic	Merkmalsextraktion (DE-588)4314440-8 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Data Mining (DE-588)4428654-5 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Mustererkennung (DE-588)4040936-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Automation (DE-588)4003957-2 gnd
topic_facet	Merkmalsextraktion Künstliche Intelligenz Data Mining Automatische Klassifikation Mustererkennung Maschinelles Lernen Automation
url	https://doi.org/10.1515/9783111339207
work_keys_str_mv	AT beyererjurgen patternrecognitionintroductionfeaturesclassifiersandprinciples AT hagmannsraphael patternrecognitionintroductionfeaturesclassifiersandprinciples AT stadlerdaniel patternrecognitionintroductionfeaturesclassifiersandprinciples

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