Research on Ensemble Multi-Instance Learning

被引：0

作者：

Huang, Bo ^{[1
]}

Cai, Zhihua ^{[1
]}

Tao, Duoxiu ^{[2
]}

Gu, Qiong ^{[1
]}

机构：

[1] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Hubei, Peoples R China

[2] Guangxi Univ, Coll Elect Engn, Nanning 530004, Peoples R China

来源：

PROGRESS IN INTELLIGENCE COMPUTATION AND APPLICATIONS | 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：

In this paper, an ensemble multi-instance classifier was designed using MISMO, CitationKNN, MDD, MIDD and MIEMDD. We apply two voting mechanisms to the ensemble multi-instance classifiers. On the musk training set, ensemble multi-instance learning performs well, and attains a modest performance increase compared to the individual classifiers.

引用

页码：200 / 204

页数：5

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