Co-training using RBF nets and different feature splits

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.