66 search results - page 1 / 14 » Learning Sparse SVM for Feature Selection on Very High Dimen... |
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
ICANN
2009
Springer
13 years 9 months ago
2009
Springer
Abstract. Many applications of machine learning involve sparse highdimensional data, where the number of input features is (much) larger than the number of data samples, d n. Predi...
MLDM
2007
Springer
13 years 10 months ago
2007
Springer
Support vector machine (SVM) has received much attention in feature selection recently because of its ability to incorporate kernels to discover nonlinear dependencies between feat...
CVPR
2004
IEEE
14 years 6 months ago
2004
IEEE
Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffer from the curse...
PKDD
2009
Springer
13 years 11 months ago
2009
Springer
Abstract. This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervisio...