An Introduction to Pattern Recognition and Machine Learning

by Paul Fieguth

Overview

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.

Product Details

ISBN-13:	9783030959951
Publisher:	Springer-Verlag New York, LLC
Publication date:	11/09/2022
Sold by:	Barnes & Noble
Format:	eBook
Sales rank:	933,155
File size:	58 MB
Note:	This product may take a few minutes to download.

About the Author

Paul Fieguth received the B.A.Sc. degree from the University of Waterloo, Canada, in 1991 and the Ph.D. degree from the Massachusetts Institute of Technology (MIT), United States, in 1995, both degrees in electrical engineering. He joined the faculty at the University of Waterloo in 1996, where he is currently Professor in Systems Design Engineering. He is a co-director of the Vision and Image Processing research group, where his research interests broadly involve machine learning for computer vision and statistical image processing. Specific interests include hierarchical algorithms for large problems, particularly in simplifying modelling and interpretation. In addition to this text, he is also the author on textbooks on Statistical Image Processing and Complex Systems.

Table of Contents

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.

Editorial Reviews

The book is an introduction to pattern recognition and machine learning. ... The book brings a balance between the analytical and experimental approaches of teaching these important subjects. It offers a great deal of examples and case studies. ... The book contains very useful appendices for refreshing mathematical concepts. Overall, this is an excellent introduction to pattern recognition and machine learning.” (Smaranda Belciug, zbMATH 1516.68002, 2023)

From the Publisher