The entropy regularization information criterion

被引:0
|
作者
Smola, AJ [1 ]
Shawe-Taylor, J [1 ]
Schölkopf, B [1 ]
Williamson, RC [1 ]
机构
[1] Australian Natl Univ, Dept Engn & RSISE, Canberra, ACT 0200, Australia
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Effective methods of capacity control via uniform convergence bounds for function expansions have been largely limited to Support Vector machines, where good bounds are obtainable by the entropy number approach. We extend these methods to systems with expansions in terms of arbitrary (parametrized) basis functions and a wide range of regularization methods covering the whole range of general linear additive models. This is achieved by a data dependent analysis of the eigenvalues of the corresponding design matrix.
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页码:342 / 348
页数:7
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