A sufficient condition for polynomial distribution-dependent learnability
A sufficient condition for polynomial distribution-dependent learnability
We investigate upper bounds on the sample-size sufficient for 'solid' learnability with respect to a probability distribution. Extending analysis of Ben-David et al. (1989, 1995) and Bendek and Itai (1991) we obtain a sufficient condition for feasible (polynomially bounded) sample-size bounds for distribution-specific (solid) learnability.
1-12
Anthony, M.
44cc9b8c-f199-4df9-a6c5-8b4a37c238b2
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
June 1997
Anthony, M.
44cc9b8c-f199-4df9-a6c5-8b4a37c238b2
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
Anthony, M. and Shawe-Taylor, J.
(1997)
A sufficient condition for polynomial distribution-dependent learnability.
Discrete Applied Mathematics, 77 (1), .
Abstract
We investigate upper bounds on the sample-size sufficient for 'solid' learnability with respect to a probability distribution. Extending analysis of Ben-David et al. (1989, 1995) and Bendek and Itai (1991) we obtain a sufficient condition for feasible (polynomially bounded) sample-size bounds for distribution-specific (solid) learnability.
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Published date: June 1997
Organisations:
Electronics & Computer Science
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Local EPrints ID: 259794
URI: http://eprints.soton.ac.uk/id/eprint/259794
ISSN: 0166-218X
PURE UUID: efa5f887-a7fc-45e4-a9a4-f6997a34a98f
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Date deposited: 17 Aug 2004
Last modified: 08 Jan 2022 08:48
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Author:
M. Anthony
Author:
J. Shawe-Taylor
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