PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variants of Unsupervised Kernel Regression: General Cost Functions
Stefan Klanke and Helge Ritter
In: European Symposium on Artificial Neural Networks (ESANN), 26-28 April 2006, Bruges, Belgium.

Abstract

We present an extension to a recent method for learning of nonlinear manifolds, which allows to incorporate general cost functions. We focus on the epsilon-insensitive loss and visually demonstrate our method on both toy and real data.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3427
Deposited By:Stefan Klanke
Deposited On:10 February 2008