A Novel Transfer-learning-oriented L2 Kernel Classifier

Based on the concept of Difference Of Density (DOD), L2 Kernel Classifier(L2KC) exhibits its good performance. However, the assumption that the training domain and testing domain are independent and identically distributed severely constrains its usefulness. In order to overcome this shortcoming, a novel classifier named Transfer Learnging-L2 Kernel Classification (TL-L2KC) is proposed for the changing environment. The proposed classifier can not only inherit the advantage of L2KC, but also deal with the problem that the distribution inconsistency between the training and testing sets which is caused by the slow change of the datasets or the training set obtained with specific constraints. As demonstrated by extensive experiments in simulation datasets and UCI benchmark datasets, the proposed classifier TL-L2KC shows the performance which is comparable to or better than that of the classical algorithms on the transfer learning classification problems.