Co-training using RBF nets and different feature splits

被引：0

作者：

Feger, Felix ^{[1
]}

Koprinska, Irena ^{[2
]}

机构：

[1] Otto Friedrich Univ Bamberg, Kapuzinerstr 16, D-96045 Bamberg, Germany

[2] Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia

来源：

2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10 | 2006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 we propose a new graph-based feature splitting algorithm maxInd, which creates a balanced split maximizing the independence between the two feature sets. We study the performance of RBF net in a co-training setting with natural, truly independent, random and maxInd split. The results show that RBF net is successful in a co-training setting, outperforming SVM and NB. Co-training is also found to be sensitive to the trade-off between the dependence of the features within a feature set, and the dependence between the feature sets.

引用

页码：1878 / +

页数：2

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