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An Introduction to Pattern Recognition and Machine Learning
Record Type:
Language materials, printed : Monograph/item
Title/Author:
An Introduction to Pattern Recognition and Machine Learning/ by Paul Fieguth.
Author:
Fieguth, Paul.
Description:
XXII, 471 p. 270 illus., 265 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Signal processing. -
Online resource:
https://doi.org/10.1007/978-3-030-95995-1
ISBN:
9783030959951
An Introduction to Pattern Recognition and Machine Learning
Fieguth, Paul.
An Introduction to Pattern Recognition and Machine Learning
[electronic resource] /by Paul Fieguth. - 1st ed. 2022. - XXII, 471 p. 270 illus., 265 illus. in color.online resource.
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.
ISBN: 9783030959951
Standard No.: 10.1007/978-3-030-95995-1doiSubjects--Topical Terms:
561459
Signal processing.
LC Class. No.: TK5102.9
Dewey Class. No.: 621.3822
An Introduction to Pattern Recognition and Machine Learning
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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.
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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.
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Mathematics and Statistics (R0) (SpringerNature-43713)
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