Estimating the moments of a random vector with applications
Estimating the moments of a random vector with applications
A general result about the quality of approximation of the mean of a distribution by its empirical estimate is proven that does not involve the dimension of the feature space. Using the kernel trick this gives also bounds the quality of approximation of higher order moments. A number of applications are derived of interest in learning theory including a new novelty detection algorithm and rigorous bounds on the Robust Minimax Classification algorithm.
47-52
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Cristianini, Nello
091768cb-dfc6-4422-827d-f520fefc4b40
2003
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Cristianini, Nello
091768cb-dfc6-4422-827d-f520fefc4b40
Shawe-Taylor, John and Cristianini, Nello
(2003)
Estimating the moments of a random vector with applications.
In Proceedings of GRETSI 2003 Conference.
.
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Conference or Workshop Item
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Abstract
A general result about the quality of approximation of the mean of a distribution by its empirical estimate is proven that does not involve the dimension of the feature space. Using the kernel trick this gives also bounds the quality of approximation of higher order moments. A number of applications are derived of interest in learning theory including a new novelty detection algorithm and rigorous bounds on the Robust Minimax Classification algorithm.
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EstimatingTheMomentsOfARandomVectorWithApplications.pdf
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Published date: 2003
Additional Information:
Invited Talk
Organisations:
Electronics & Computer Science
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Local EPrints ID: 260372
URI: http://eprints.soton.ac.uk/id/eprint/260372
PURE UUID: a6ca3390-a25d-4640-bf0a-15406213d203
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Date deposited: 26 Jan 2005
Last modified: 14 Mar 2024 06:38
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Author:
John Shawe-Taylor
Author:
Nello Cristianini
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