209 search results - page 5 / 42 » Evaluating Feature Selection for SVMs in High Dimensions |
CORR
2006
Springer
14 years 10 months ago
2006
Springer
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
ICTAI
2009
IEEE
14 years 8 months ago
2009
IEEE
The selection of features for classification, clustering and approximation is an important task in pattern recognition, data mining and soft computing. For real-valued features, th...
ICML
2009
IEEE
15 years 11 months ago
2009
IEEE
This paper addresses feature selection techniques for classification of high dimensional data, such as those produced by microarray experiments. Some prior knowledge may be availa...
ICML
2010
IEEE
14 years 11 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...
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BMCBI
2007
14 years 10 months ago
2007
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...