JMLR: Sparseness of Support Vector Machines

[[Redistributed from JMLR announce]]

~From: "David 'Pablo' Cohn" <[EMAIL PROTECTED]>
~Date: 26 Nov 2003 11:56:13 -0800
~Subject: jmlr-announce: Sparseness of Support Vector Machines

The Journal of Machine Learning Research (www.jmlr.org) is pleased to
announce publication of a new paper:
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Sparseness of Support Vector Machines
Ingo Steinwart
JMLR 4(Nov):1071-1105, 2003.

Abstract

Support vector machines (SVMs) construct decision functions that are
linear combinations of kernel evaluations on the training set. The
samples with non-vanishing coefficients are called support vectors. In
this work we establish lower (asymptotical) bounds on the number of
support vectors. On our way we prove several results which are of great
importance for the understanding of SVMs. In particular, we describe to
which "limit" SVM decision functions tend, discuss the corresponding
notion of convergence and provide some results on the stability of SVMs
using subdifferential calculus in the associated reproducing kernel
Hilbert space.

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This paper, and all previous papers in Volume 4 are available
electronically at http://www.jmlr.org in PostScript and PDF formats. The
papers of Volumes 1, 2 and 3 are also available electronically from the
JMLR website, and in hardcopy from the MIT Press; please see
http://mitpress.mit.edu/JMLR for details.

-David Cohn, <[EMAIL PROTECTED]>

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