Overview
- Authors:
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Jack Sklansky
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Department of Electrical Engineering, University of California at Irvine, Irvine, USA
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Gustav N. Wassel
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Department of Electronic and Electrical Engineering, California Polytechnic State University, San Luis Obispo, USA
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Table of contents (7 chapters)
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- Jack Sklansky, Gustav N. Wassel
Pages 1-30
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- Jack Sklansky, Gustav N. Wassel
Pages 31-78
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- Jack Sklansky, Gustav N. Wassel
Pages 79-121
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- Jack Sklansky, Gustav N. Wassel
Pages 122-169
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- Jack Sklansky, Gustav N. Wassel
Pages 170-234
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- Jack Sklansky, Gustav N. Wassel
Pages 235-283
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- Jack Sklansky, Gustav N. Wassel
Pages 284-312
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Back Matter
Pages 313-335
About this book
This book is the outgrowth of both a research program and a graduate course at the University of California, Irvine (UCI) since 1966, as well as a graduate course at the California State Polytechnic University, Pomona (Cal Poly Pomona). The research program, part of the UCI Pattern Recogni tion Project, was concerned with the design of trainable classifiers; the graduate courses were broader in scope, including subjects such as feature selection, cluster analysis, choice of data set, and estimates of probability densities. In the interest of minimizing overlap with other books on pattern recogni tion or classifier theory, we have selected a few topics of special interest for this book, and treated them in some depth. Some of this material has not been previously published. The book is intended for use as a guide to the designer of pattern classifiers, or as a text in a graduate course in an engi neering or computer science curriculum. Although this book is directed primarily to engineers and computer scientists, it may also be of interest to psychologists, biologists, medical scientists, and social scientists.
Authors and Affiliations
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Department of Electrical Engineering, University of California at Irvine, Irvine, USA
Jack Sklansky
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Department of Electronic and Electrical Engineering, California Polytechnic State University, San Luis Obispo, USA
Gustav N. Wassel
