Pattern Recognition and Classification - Geoff Dougherty

Pattern Recognition and Classification

An Introduction

(Autor)

Buch | Softcover
196 Seiten
2017 | Softcover reprint of the original 1st ed. 2013
Springer-Verlag New York Inc.
978-1-4939-5335-6 (ISBN)
117,69 inkl. MwSt
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 laterchapters.

This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.

Geoff Dougherty is a Professor of Applied Physics and Medical Imaging at California State University, Channel Islands.  He is the Author of Springer's Medical Image Processing, Techniques and Applications

Introduction.- Classification.- Nonmetric Methods.- Statistical Pattern Recognition.- Supervised Learning.- Nonparametric Learning.- Feature Extraction and Selection.- Unsupervised Learning.- Estimating and Comparing Classifiers.- Projects

Erscheinungsdatum
Zusatzinfo 104 Illustrations, color; 54 Illustrations, black and white; XI, 196 p. 158 illus., 104 illus. in color.
Verlagsort New York
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Mathematik / Informatik Mathematik Analysis
Mathematik / Informatik Mathematik Angewandte Mathematik
Naturwissenschaften Biologie
Naturwissenschaften Physik / Astronomie Optik
Technik Elektrotechnik / Energietechnik
Schlagworte Bayesian Decision Theory • Clustering Techniques • feature extraction • machine learning • Neural networks • Object recognition • pattern recognition
ISBN-10 1-4939-5335-4 / 1493953354
ISBN-13 978-1-4939-5335-6 / 9781493953356
Zustand Neuware
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