|
|
 | | From: | Redistributed | | Subject: | JMLR: Diffusion Kernels on Statistical Manifolds | | Date: | Thu, 20 Jan 2005 18:55:20 GMT |
|
|
 | [[Redistributed from JMLR announce]]
~From: elm@cs.umass.edu
~Date: Thu, 13 Jan 2005 09:36:21 -0500
~Subject: [Jmlr-announce] Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research (www.jmlr.org) is pleased to
announce publication of a new paper:
------------------------------------------------------------------------
-------
Diffusion Kernels on Statistical Manifolds
John Lafferty and Guy Lebanon
JMLR 6 (Jan): 129--163, 2005
Abstract
A family of kernels for statistical learning is introduced that
exploits the geometric structure of statistical models. The kernels are
based on the heat equation on the Riemannian manifold defined by the
Fisher information metric associated with a statistical family, and
generalize the Gaussian kernel of Euclidean space. As an important
special case, kernels based on the geometry of multinomial families are
derived, leading to kernel-based learning algorithms that apply
naturally to discrete data. Bounds on covering numbers and Rademacher
averages for the kernels are proved using bounds on the eigenvalues of
the Laplacian on Riemannian manifolds. Experimental results are
presented for document classification, for which the use of multinomial
geometry is natural and well motivated, and improvements are obtained
over the standard use of Gaussian or linear kernels, which have been
the standard for text classification.
------------------------------------------------------------------------
------
This paper and previous papers are available electronically at
http://www.jmlr.org in PDF format. The papers of Volumes 1-4 were also
published in hardcopy by MIT Press; please see
http://mitpress.mit.edu/JMLR for details. Volume 5 and subsequent
volumes will be printed in hardcopy by Microtome Publishing. Please see
http://www.mtome.com/Publications/jmlr.html for details and ordering
information.
-Erik G. Learned-Miller
elm@cs.umass.edu
_______________________________________________
Jmlr-announce mailing list
Jmlr-announce@lists.csail.mit.edu
http://lists.csail.mit.edu/mailman/listinfo/jmlr-announce
[ comp.ai is moderated. To submit, just post and be patient, or if ]
[ that fails mail your article to , and ]
[ ask your news administrator to fix the problems with your system. ]
|
|
|
