A kernel method for classification
D MacDonald, J Koetsier, E Corchado, C Fyfe… - MICAI 2004: Advances …, 2004 - Springer
D MacDonald, J Koetsier, E Corchado, C Fyfe, J Corchado
MICAI 2004: Advances in Artificial Intelligence: Third Mexican International …, 2004•SpringerAbstract Kernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel
technique developed to facilitate the clustering of complex data effectively and efficiently and
that is characterised for converging remarkably quickly. The combination of Maximum
Likelihood Hebbian Learning Scale Invariant Map and the Kernel Space provides a very
smooth scale invariant quantisation which can be used as a clustering technique. The
efficiency of this method have been used to analyse an oceanographic problem.
technique developed to facilitate the clustering of complex data effectively and efficiently and
that is characterised for converging remarkably quickly. The combination of Maximum
Likelihood Hebbian Learning Scale Invariant Map and the Kernel Space provides a very
smooth scale invariant quantisation which can be used as a clustering technique. The
efficiency of this method have been used to analyse an oceanographic problem.
Abstract
Kernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel technique developed to facilitate the clustering of complex data effectively and efficiently and that is characterised for converging remarkably quickly. The combination of Maximum Likelihood Hebbian Learning Scale Invariant Map and the Kernel Space provides a very smooth scale invariant quantisation which can be used as a clustering technique. The efficiency of this method have been used to analyse an oceanographic problem.
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