We present and discuss several spatiotemporal kernels designed to mine real-life and simulated data in support of drought prediction. We implement and empirically validate these k...
This paper describes a local ensemble kernel learning technique to recognize/classify objects from a large number of diverse categories. Due to the possibly large intraclass featu...
Kernel methods provide an efficient mechanism to derive nonlinear algorithms. In classification problems as well as in feature extraction, kernel-based approaches map the original...
We propose a new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces. Unlike previous kernel...
Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernh...
Visualization of non-vectorial objects is not easy in practice due to their lack of convenient vectorial representation. Representative approaches are Kernel PCA and Kernel Laplac...