We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem. In the past, two efficient methods, i.e., Semi-Infinite Linear Prog...
Complex biological data generated from various experiments are stored in diverse data types in multiple datasets. By appropriately representing each biological dataset as a kernel ...
Hiroaki Tanabe, Tu Bao Ho, Canh Hao Nguyen, Saori ...
Many kernel learning methods have to assume parametric forms for the target kernel functions, which significantly limits the capability of kernels in fitting diverse patterns. Som...
Although Support Vector Machines(SVM) succeed in classifying several image databases using image descriptors proposed in the literature, no single descriptor can be optimal for ge...
Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different for...