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An introduction to pattern recogniti...

Fieguth, Paul.

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An introduction to pattern recognition and machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An introduction to pattern recognition and machine learning/ by Paul Fieguth.
作者:	Fieguth, Paul.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xxii, 471 p. :ill. (chiefly color), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Pattern perception. -
電子資源:	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. - Cham :Springer International Publishing :2022. - xxii, 471 p. :ill. (chiefly color), digital ;24 cm.

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:

649387
Pattern perception.

LC Class. No.: Q327

Dewey Class. No.: 006.4

An introduction to pattern recognition and machine learning
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505 0 $a 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.
520 $a 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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