Multiplicative updates for large margin classifiers

被引：10

作者：

Sha, F

Saul, LK

Lee, DD

机构：

[1] Univ Penn, Dept Comp & Informat Sci, Philadelphia, PA 19104 USA

[2] Univ Penn, Dept Elect & Syst Engn, Philadelphia, PA 19104 USA

来源：

LEARNING THEORY AND KERNEL MACHINES | 2003年 / 2777卷

关键词：

D O I：

10.1007/978-3-540-45167-9_15

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Various problems in nonnegative quadratic programming arise in the training of large margin classifiers. We derive multiplicative updates for these problems that converge monotonically to the desired solutions for hard and soft margin classifiers. The updates differ strikingly in form from other multiplicative updates used in machine learning. In this paper, we provide complete proofs of convergence for these updates and extend previous work to incorporate sum and box constraints in addition to nonnegativity.

引用

页码：188 / 202

页数：15

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