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Cross Validation and MLP Architecture Selection

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Permanent URL	http://hdl.lib.byu.edu/1877/2419
Title	Cross Validation and MLP Architecture Selection
BYU Creator	Andersen, Timothy L. Martinez, Tony R.
Keywords	cross validation; optimal network architecture; MLP architecture
Description/Abstract	The performance of cross validation (CV) based MLP architecture selection is examined using 14 real world problem domains. When testing many different network architectures the results show that CV is only slightly more likely than random to select the optimal network architecture, and that the strategy of using the simplest available network architecture performs better than CV in this case. Experimental evidence suggests several reasons for the poor performance of CV. In addition, three general strategies which lead to significant increase in the performance of CV are proposed. While this paper focuses on using CV to select the optimal MLP architecture, the strategies are also applicable when CV is used to select between several different learning models, whether the models are neural networks, decision trees, or other types of learning algorithms. When using these strategies the average generalization performance of the network architecture which CV selects is significantly better than the performance of several other well known machine learning algorithms on the data sets tested.
Source	Andersen, T. L. and Martinez, T. R., "Cross Validation and MLP Architecture Selection", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99, CD paper #192, 1999.;
Publisher	IEEE
Date Original	1999
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) 1999 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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