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＜E-Book＞
An Introduction to Pattern Recognition and Machine Learning / by Paul Fieguth

Edition	1st ed. 2022.
Publisher	(Cham : Springer International Publishing : Imprint: Springer)
Year	2022
Language	English
Size	XXII, 471 p. 270 illus., 265 illus. in color : online resource
Authors	*Fieguth, Paul author SpringerLink (Online service)
Subjects	LCSH:Signal processing LCSH:Pattern recognition systems LCSH:System theory LCSH:Data mining FREE:Digital and Analog Signal Processing FREE:Automated Pattern Recognition FREE:Complex Systems FREE:Data Mining and Knowledge Discovery
Notes	Chapter 1. Overview -- Chapter 2. Introduction to Pattern Recognition -- Chapter 3. Learning -- Chapter 4. Representing Patterns -- Chapter 5. Feature Extraction and Selection -- Chapter 6. Distance-Based Classification -- Chapter 7. Inferring Class Models -- Chapter 8. Statistics-Based Classification -- Chapter 9. Classifier Testing and Validation -- Chapter 10. Discriminant-Based Classification -- Chapter 11. Ensemble Classification -- Chapter 12. Model-Free Classification -- Chapter 13. Conclusions and Directions The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated computational libraries to understand and appreciate pattern recognition; rather the fundamental concepts and insights, eminently teachable at the undergraduate level, motivate this text. This book provides methods of analysis that the reader can realistically undertake on their own, supported by real-world examples, case-studies, and worked numerical / computational studies HTTP:URL=https://doi.org/10.1007/978-3-030-95995-1

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