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Machine learning, neural and statistical classification. (English) Zbl 0827.68094

Ellis Horwood Series in Artificial Intelligence. New York, NY: Ellis Horwood (ISBN 0-13-106360-X). xiv, 289 p. (1994).
The articles of this volume will not be indexed individually.
The present text has been produced by a variety of authors, from widely differing backgrounds, but with the common aim of making the results of the StatLog project accessible to wide range of workers in the fields of machine learning, statistics and neural networks, and to help the cross- fertilisation of ideas between these groups.
After discussing the general classification problem in Chapter 2, the next 4 chapters detail the methods that have been investigated, divided up according to broad headings of Classical statistics, modern statistical techniques. Decision Trees and Rules, and Neural Networks. The next part of the book concerns the evaluation experiments, and includes chapters on evaluation criteria, a survey of previous comparative studies, a description of the data-sets and the results for the different methods, and an analysis of the results which explores the characteristics of data-sets that make them suitable for particular approaches: we might call this “machine learning on machine learning”. The conclusions concerning the experiments are summarised in Chapter 11.
The final chapters of the book broaden the interpretation of the basic classification problem. The fundamental theme of representing knowledge using different formalisms is discussed with relation to constructing classification techniques, followed by a summary of current approaches to dynamic control now arising from a rephrasing of the problem in terms of classification and learning.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science
62H30 Classification and discrimination; cluster analysis (statistical aspects)
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