DocumentCode
3060457
Title
Direct calculation of predictions for K29/K29*
Author
Burford, Brian
Author_Institution
Univ. of London, Egham
fYear
2007
fDate
13-15 Dec. 2007
Firstpage
458
Lastpage
463
Abstract
Implementation of the K29 learning algorithm presents the problem of finding a root of an often non-invertible function. Numerical methods giving approximations to these roots have small numerical inaccuracies. These inaccuracies, despite possibly seeming negligible, can accumulate quickly when applied to K29. The main mathematical result of this paper presents a simple but novel derivation of two formulae, which directly calculate predictions for the K29 (and the regularised version K29*) learning algorithms. We present comparisons between this new implementation and the numerical method through empirical investigation.
Keywords
approximation theory; learning (artificial intelligence); K29 learning algorithm; K29* learning algorithm; approximations; noninvertible function; numerical inaccuracy; Application software; Computer science; Machine learning; Machine learning algorithms; Protocols; Terminology; Tin; Upper bound;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on
Conference_Location
Cincinnati, OH
Print_ISBN
978-0-7695-3069-7
Type
conf
DOI
10.1109/ICMLA.2007.56
Filename
4457272
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