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Titolo
A Kernel Method for Classification
Autor(es)
Soggetto
Computer Science
Fecha de publicación
2004/04
Editore
Springer Science + Business Media
Citación
MICAI 2004: Advances in Artificial Intelligence Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 2972, pp. 823-832.
Resumen
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.
URI
ISBN
978-3-540-21459-5 (Print) / 978-3-540-24694-7 (Online)
ISSN
0302-9743 (Print) / 1611-3349 (Online)
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