Shrinking the tube:: A new support vector regression algorithm

被引：0

作者：

Schölkopf, B ^{[1
]}

Bartlett, P ^{[1
]}

Smola, A ^{[1
]}

Williamson, R ^{[1
]}

机构：

[1] GMD FIRST, D-12489 Berlin, Germany

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 11 | 1999年 / 11卷

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：

A new algorithm for Support Vector regression is described. For a priori chosen v, it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction v of the data points lie outside. Moreover, it is shown how to use parametric tube shapes with non-constant radius. The algorithm is analysed theoretically and experimentally.

引用

页码：330 / 336

页数：7

共 10 条

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[9] Stitson MO, 1999, ADVANCES IN KERNEL METHODS, P285
[10] Vapnik V, 1999, NATURE STAT LEARNING

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