Transfer Learning via Semi-supervised Boosting Method
Transfer learning aims at reusing existing instances from other related domains to help learning models for the target domain.Existing algorithms in instance-transfer learning might easily suffer from the problem of overfitting.To address this problem,we propose to incorporate additional unlabeled instances from the target domain,so that more domain knowledge can be brought into the training process.Specifically,under the generalized framework of boosting methods,we show that a semi-supervised boosting method can be applied to help re-weighting the source domain instances,making the final classifiers less sensitive to the small amount of labeled instances in the target domain.Extensive experiments confirm the efficiency of the new algorithm.