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Abstract: We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can generalize well, and uses a subset of nonzero weighted variables found by the linear models to produce a final nonlinear model. The method exploits the fact that a linear SVM (no kernels) with # 1 -norm regularization inherently performs variable selection as a side-e#ect of minimizing capacity... (Update)
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BibTeX entry: (Update)
@misc{ machines-journal,
author = "Support Vector Machines",
title = "Journal of Machine Learning Research 1 (2002) 1-48 Submitted 5/02; Published
xx/03 Dimensionality Reduction via Sparse",
url = "citeseer.ist.psu.edu/766331.html" }
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