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ICML
2010
IEEE
13 years 9 months ago
2010
IEEE
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
CSC
2006
13 years 10 months ago
2006
In this paper, a new method is proposed in order to evaluate the stochastic solution of linear random differential equation. The method is based on the combination of the probabili...
CVPR
2007
IEEE
14 years 10 months ago
2007
IEEE
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dimensionality reduction, Linear Discriminant Analysis (LDA) is one of the most po...
IJON
2007
13 years 8 months ago
2007
This paper addresses the problem of variable ranking for Support Vector Regression. The relevance criteria that we proposed are based on leave-one-out bounds and some variants and...
KDD
2007
ACM
14 years 9 months ago
2007
ACM
The kernel function plays a central role in kernel methods. In this paper, we consider the automated learning of the kernel matrix over a convex combination of pre-specified kerne...