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The 2006 IEEE International Joint Conference on Neural Network Proceedings

DOI: 10.1109/ijcnn.2006.246909

The 2006 IEEE International Joint Conference on Neural Network Proceedings

DOI: 10.1109/ijcnn.2006.1716339

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Co-training using RBF Nets and Different Feature Splits

Proceedings article published in 2006 by F. Feger, I. Koprinska ORCID
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This paper is available in a repository.

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Abstract

In this paper we propose a new graph-based feature splitting algorithm maxlnd, 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 maxlnd 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.