Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation

To further incorporate unlabeled samples with labeled ones For well generalization capability, it is required to collect in model learning, the kernel prediction framework is developed and label plenty of training samples following the same into semi-supervised setting by formulating a regularized distribution of test samples, however, which is extremely expensive Fredholm integral equation (Que, Belkin, and Wan in practical applications. To relieve the contradiction 2014). Although this development is proven theoretically between generalization performance and label cost, domain and empirically to be effective in noise suppression, the performance adaptation (Pan and Yang 2010) has been proposed to transfer heavily depends on the choice of a single predefined knowledge from a relevant but different source domain kernel function. The reason is that its solution is based with sufficient labeled data to the target domain. It gains increased on Representer Theorem (Scholkopf and Smola 2001) in the importance in many applied areas of machine learning Reproducing Kernel Hilbert Space (RKHS) induced by the (Daumé III 2007; Pan et al. 2011), including natural language kernel. More importantly, the kernel methods are not developed processing, computer vision and WiFi localization.