Eliminating Redundant Training Data Using Unsupervised Clustering TechniquesReportar como inadecuado


Eliminating Redundant Training Data Using Unsupervised Clustering Techniques


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Abstract

Training data for supervised learning neural networks can be clustered such that the input-output pairs in each cluster are redundant. Redundant training data can adversely affect training time. In this paper we apply two clustering algorithms, ART2 -A and the Generalized Equality Classifier, to identify training data clusters and thus reduce the training data and training time. The approach is demonstrated for a high dimensional nonlinear continuous time mapping. The demonstration shows six-fold decrease in training time at little or no loss of accuracy in the handling of evaluation data.Rights

Copyright 1993 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and - or special permission.

CAS-CNS Technical Reports -



Autor: Gonsalves, Paul G. - Snorrason, Magnus - Caglayan, Alper K. - -

Fuente: https://open.bu.edu/







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