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PPSN
1990
Springer

Feature Construction for Back-Propagation

13 years 7 months ago
Feature Construction for Back-Propagation
T h e ease of learning concepts f r o m examples in empirical machine learning depends on the attributes used for describing the training d a t a . We show t h a t decision-tree based feature construction can be used to improve the performance of back-propagation ( B P ) , an artificial neural network a l g o r i t h m , b o t h in terms of the convergence speed a n d the number of epochs taken by the BP a l g o r i t h m to converge. We use disjunctive concepts to illustrate feature construction, and describe a measure of feature quality a n d concept difficulty. We show t h a t a reduction in the difficulty of the concepts to be learned by constructing better representations increases the performance of BP considerably. 1 I n t r o d u c t i o n Recent progress in artificial neural networks ( A N N s ) and their use in disparate domains have spurred the interests of researchers in studying various means of i m p r o v i n g t h e m . A N N s have shown promising results for a number...
Selwyn Piramuthu
Added 11 Aug 2010
Updated 11 Aug 2010
Type Conference
Year 1990
Where PPSN
Authors Selwyn Piramuthu
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