Title
Informational Energy Kernel for LVQ
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
9-11-2005
Abstract
We describe a kernel method which uses the maximization of Onicescu’s informational energy as a criteria for computing the relevances of input features. This adaptive relevance determination is used in combination with the neural-gas and the generalized relevance LVQ algorithms. Our quadratic optimization function, as an L 2 type method, leads to linear gradient and thus easier computation. We obtain an approximation formula similar to the mutual information based method, but in a more simple way.
Recommended Citation
Caţaron A., Andonie R. (2005) Informational Energy Kernel for LVQ. In: Duch W., Kacprzyk J., Oja E., Zadrożny S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_95
Journal
Lecture Notes in Computer Science
Rights
© Springer-Verlag Berlin Heidelberg 2005
Comments
This article was originally published in Lecture Notes in Computer Science. The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.