Overview
- Authors:
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Diane D. Wolff
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University of Arizona, Tucson, USA
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Michael L. Parsons
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Department of Chemistry, Arizona State University, Tempe, USA
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Table of contents (4 chapters)
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Front Matter
Pages i-xiii
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- Diane D. Wolff, Michael L. Parsons
Pages 1-15
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- Diane D. Wolff, Michael L. Parsons
Pages 17-108
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- Diane D. Wolff, Michael L. Parsons
Pages 109-160
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- Diane D. Wolff, Michael L. Parsons
Pages 161-172
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Back Matter
Pages 173-223
About this book
An attempt is made in this book to give scientists a detailed working knowledge of the powerful mathematical tools available to aid in data interpretation, especially when con fronted with large data sets incorporating many parameters. A minimal amount of com puter knowledge is necessary for successful applications, and we have tried conscien tiously to provide this in the appropriate sections and references. Scientific data are now being produced at rates not believed possible ten years ago. A major goal in any sci entific investigation should be to obtain a critical evaluation of the data generated in a set of experiments in order to extract whatever useful scientific information may be present. Very often, the large number of measurements present in the data set does not make this an easy task. The goals of this book are thus fourfold. The first is to create a useful reference on the applications of these statistical pattern recognition methods to the sciences. The majority of our discussions center around the fields of chemistry, geology, environmen tal sciences, physics, and the biological and medical sciences. In Chapter IV a section is devoted to each of these fields. Since the applications of pattern recognition tech niques are essentially unlimited, restricted only by the outer limitations of.
Authors and Affiliations
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University of Arizona, Tucson, USA
Diane D. Wolff
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Department of Chemistry, Arizona State University, Tempe, USA
Michael L. Parsons
