Recent Advances and Trends in Large-Scale Kernel Methods

被引：13

作者：

Kashima, Hisashi ^{[1
]}

Ide, Tsuyoshi ^{[1
]}

Kato, Tsuyoshi ^{[2
]}

Sugiyama, Masashi ^{[3
]}

机构：

[1] IBM Res Corp, Tokyo Res Lab, Yamato 2428502, Japan

[2] Ochanomizu Univ, Ctr Informat Biol, Tokyo 1128610, Japan

[3] Tokyo Inst Technol, Dept Comp Sci, Tokyo 1528552, Japan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2009年 / E92D卷 / 07期

关键词：

kernel methods; support vector machines; kernel trick; low-rank approximation; optimilzation; structured data; DIMENSIONALITY REDUCTION; SELECTION;

D O I：

10.1587/transinf.E92.D.1338

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Kernel methods such as the support vector machine are one of the most successful algorithms in modern machine learning. Their advantage is that linear algorithms are extended to non-linear scenarios in a straightforward way by the use of the kernel trick. However, naive use of kernel methods is computationally expensive since the computational complexity typically scales cubically with respect to the number of training samples. In this article. we review recent advances in the kernel methods. with emphasis on scalability for massive problems.

引用

收藏

页码：1338 / 1353

页数：16

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