We devise a boosting approach to classification and regression based on column generation using a mixture of kernels. Traditional kernel methods construct models based on a single...
Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification m...
We address the problem of feature selection in a kernel space to select the most discriminative and informative features for classification and data analysis. This is a difficult ...
Bin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng...
We study the use of kernel subspace methods that learn low-dimensional subspace representations for classification tasks. In particular, we propose a new method called kernel weigh...