| Evolution and generalization of a single neurone: I. Single-layer perceptron as seven statistical classifiers. | |
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MedLine Citation:
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PMID: 12662838 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Unlike many other investigations on this topic, the present one considers the non-linear single-layer perceptron (SLP) as a process in which the weights of the perceptron are increasing, and the cost function of the sum of squares is changing gradually. During the backpropagation training, the decision boundary of of SLP becomes identical or close to that of seven statistical classifiers: (1) the Euclidean distance classifier, (2) the regularized linear discriminant analysis, (3) the standard Fisher linear discriminant function, (4) the Fisher linear discriminant function with a pseudoinverse covariance matrix, (5) the generalized Fisher discriminant function, (6) the minimum empirical error classifier, and (7) the maximum margin classifier. In order to obtain a wider range of classifiers, five new complexity-control techniques are proposed: target value control, moving of the learning data centre into the origin of coordinates, zero weight initialization, use of an additional negative weight decay term called "anti-regularization", and use of an exponentially increasing learning step. Which particular type of classifier will be obtained depends on the data, the cost function to be minimized, the optimization technique and its parameters, and the stopping criteria. |
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Authors:
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S Raudys |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Neural networks : the official journal of the International Neural Network Society Volume: 11 ISSN: 0893-6080 ISO Abbreviation: Neural Netw Publication Date: 1998 Mar |
Date Detail:
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Created Date: 2010-09-24 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8805018 Medline TA: Neural Netw Country: United States |
Other Details:
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Languages: eng Pagination: 283-96 Citation Subset: - |
Affiliation:
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Institute of Mathematics and Informatics, Akademijos 4, Vilnius 2600, Lithuania. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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