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商品詳細
Pattern Recognition and Classification: An Introduction. Softcover reprint of the original 1st ed. 2013
・ISBN 978-1-4939-5335-6 paper EUR 109.99
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お気に入り
★★★
| 著者・編者 | Dougherty, Geoff, |
|---|---|
| 出版社 | (Springer-Verlag New York Inc., US) |
| 出版年月 | 2017 |
| ページ数 | 196 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-40587＞ |
解説
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 laterchapters.
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 laterchapters.
This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.