RANSAC-SVM for Large-Scale Datasets

被引：0

作者：

Nishida, Kenji ^{[1
]}

Kurita, Takio ^{[1
]}

机构：

[1] Natl Inst Adv Ind Sci & Technol, Neurosci Res Inst, Tsukuba, Ibaraki 3058568, Japan

来源：

19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 | 2008年

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：

Support Vector Machines (SVMs), though accurate, are still difficult to solve large-scale applications, due to the computational and storage requirement. To relieve this problem, we propose RANSAC-SVM method, which trains a number of small SVMs for randomly selected subsets of training set, while tuning their parameters to fit SVMs to whole training set. RANSAC-SVM achieves good generalization performance, which close to the Bayesian estimation, with small subset of the training samples, and outperforms the full SVM solution in some condition.

引用

页码：3767 / 3770

页数：4

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