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Title: An Introduction to Pattern Recognition and Machine Learning ( EBook/) / by Paul Fieguth. Dewey Class: 621.3822 Author: Fieguth, Paul. author. Edition statement: 1st ed. 2022. Publication: Cham : : Springer International Publishing : : Imprint: Springer,, 2022. Other name(s): SpringerLink (Online service) Physical Details: XXII, 471 p. 270 illus., 265 illus. in color. : online resource. ISBN: 9783030959951 Mode of acces to digital resource: Digital reproduction.- Cham : Springer International Publishing, 2022. - Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format. System details note: Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). Summary Note: 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.: Contents note: 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. ------------------------------ *** Non c'è alcun posseduto per questo Record *** -----------------------------------------------
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