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Constructing High Order Perceptrons with Genetic Algorithms

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Permanent URL	http://hdl.lib.byu.edu/1877/2418
Title	Constructing High Order Perceptrons with Genetic Algorithms
BYU Creator	Andersen, Timothy L. Martinez, Tony R.
Keywords	constructive induction; minimality; generalization; feature sets
Description/Abstract	Constructive induction, which is defined to be the process of constructing new and useful features from existing ones, has been extensively studied in the literature. Since the number of possible high order features for any given learning problem is exponential in the number of input attributes (where the order of a feature is defined to be the number of attributes of which it is composed), the main problem faced by constructive induction is in selecting which features to use out of this exponentially large set of potential features. For any feature set chosen the desirable characteristics are minimality and generalization performance. This paper uses a combination of genetic algorithms and linear programming techniques to generate feature sets. The genetic algorithm searches for higher order features while at the same time seeking to minimize the size of the feature set in order to produce a feature set with good generalization accuracy. The features chosen are used as inputs to a high order perceptron network, which is trained with an interior point linear programming method. Performance on a holdout set is used in conjunction with complexity penalization in order to insure that the final feature set generated by the genetic algorithm does not overfit the training data.
Source	Andersen, T. L. and Martinez, T. R., "Constructing High Order Perceptrons with Genetic Algorithms", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'98, pp. 1920-1925, 1998.;
Publisher	IEEE
Date Original	1998
Date Digital	2010-09-09
Language	English eng en
Publication Type	Peer-Reviewed Articles Conference Proceedings
Type	Text
Format	application/pdf
Owning Institution	Brigham Young University
BYU College	College of Physical and Mathematical Sciences
BYU Department	Computer Science
Copyright Status	(c) 1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.;
Copyright Use Information	http://www.lib.byu.edu/generic_copyright.html

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