Multiple Instance Support Vector Machines With Latent Variable Description

被引：0

作者：

Lu, Lianjiang ^{[1
]}

Li, Wei ^{[1
]}

Wang, Liabao ^{[1
]}

Zhang, Yafei ^{[1
]}

Li, Yang ^{[1
]}

Bao, Lei ^{[1
]}

机构：

[1] PLA Univ Sci & Technol, Coll Command Informat Syst, Nanjing, Jiangsu, Peoples R China

来源：

2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD) | 2013年

关键词：

Multiple instance learning; Support vector machines; Latent variable models; Stochastic gradient descent;

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：

In this paper, the latent variable model is adopted to re-describe MI-SVM and its feature mapping variants. MI-SVM with latent variable description and the corresponding stochastic optimization learning algorithm are proposed. In the Musk and Corel datasets, the proposed algorithm achieves higher predicting accuracy and faster learning speed, with strong stability and robustness for parameters and noise.

引用

页码：433 / 438

页数：6

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