A Unified Framework of Binary Classifiers Ensemble for Multi-class Classification

被引：0

作者：

Takenouchi, Takashi ^{[1
]}

Ishii, Shin ^{[2
]}

机构：

[1] Future Univ Hakodate, 116-2 Kamedanakano, Hakodate, Hokkaido 0418655, Japan

[2] Kyoto Univ, Grad Sch Informat, Uji, Kyoto 6100011, Japan

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2012, PT II | 2012年 / 7664卷

关键词：

Ensemble learning; Mixture of divergences;

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：

We present a novel methods for multi-class classification by ensemble of binary classifiers for multi-class classification. The proposed method is characterized by a minimization problem of weighted divergences, and includes a lot of conventional methods as special cases. We discuss relationship between the proposed method and conventional methods and statistical properties of the proposed method. A small experiment shows that the proposed method can effectively incorporate information of multiple binary classifiers into multi-class classifier.

引用

页码：375 / 382

页数：8

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