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Computer systems that learn
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The article was published on 1990-10-15 and is currently open access. It has received 1129 citations till now. The article focuses on the topics: Computer network programming.read more
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Journal ArticleDOI
Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Book
A Probabilistic Theory of Pattern Recognition
TL;DR: The Bayes Error and Vapnik-Chervonenkis theory are applied as guide for empirical classifier selection on the basis of explicit specification and explicit enforcement of the maximum likelihood principle.
Book ChapterDOI
Irrelevant features and the subset selection problem
TL;DR: A method for feature subset selection using cross-validation that is applicable to any induction algorithm is described, and experiments conducted with ID3 and C4.5 on artificial and real datasets are discussed.
Book
An introduction to neural networks
TL;DR: An Introduction to Nueral Networks will be warmly welcomed by a wide readership seeking an authoritative treatment of this key subject without an intimidating level of mathematics in the presentation.
Book ChapterDOI
Supervised and unsupervised discretization of continuous features
TL;DR: Binning, an unsupervised discretization method, is compared to entropy-based and purity-based methods, which are supervised algorithms, and it is found that the performance of the Naive-Bayes algorithm significantly improved when features were discretized using an entropy- based method.