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Pattern Recognition and Classification [electronic resource] : An Introduction / by Geoff Dougherty.

By: Dougherty, Geoff [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: XI, 196 p. 158 illus., 104 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461453239.Subject(s): Computer science | Pattern recognition | Bioinformatics | Computational biology | Algorithms | Statistical physics | Computer Science | Pattern Recognition | Nonlinear Dynamics | Signal, Image and Speech Processing | Computer Appl. in Life Sciences | AlgorithmsAdditional physical formats: Printed edition:: No titleDDC classification: 006.4 Online resources: Click here to access online
Contents:
Introduction -- Classification -- Nonmetric Methods -- Statistical Pattern Recognition -- Supervised Learning -- Nonparametric Learning -- Feature Extraction and Selection -- Unsupervised Learning -- Estimating and Comparing Classifiers -- Projects.
In: Springer eBooksSummary: 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 estimating classifier performance and combining classifiers, and details of particular project applications are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.
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Introduction -- Classification -- Nonmetric Methods -- Statistical Pattern Recognition -- Supervised Learning -- Nonparametric Learning -- Feature Extraction and Selection -- Unsupervised Learning -- Estimating and Comparing Classifiers -- Projects.

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 estimating classifier performance and combining classifiers, and details of particular project applications are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.

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