Pattern Recognition and Classification : An Introduction
Book Details
Format
Hardback or Cased Book
ISBN-10
1461453224
ISBN-13
9781461453222
Publisher
Springer-Verlag New York Inc.
Imprint
Springer-Verlag New York Inc.
Country of Manufacture
US
Country of Publication
GB
Publication Date
Oct 29th, 2012
Print length
196 Pages
Weight
458 grams
Dimensions
24.10 x 16.00 x 1.70 cms
Product Classification:
Pattern recognition
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The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition.
The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner.
Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters.
This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.
Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters.
This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.
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