Title :
Direct calculation of predictions for K29/K29*
Author_Institution :
Univ. of London, Egham
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;
Conference_Titel :
Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on
Conference_Location :
Cincinnati, OH
Print_ISBN :
978-0-7695-3069-7
DOI :
10.1109/ICMLA.2007.56