We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
Kernel based methods such as Support Vector Machine (SVM) have provided successful tools for solving many recognition problems. One of the reason of this success is the use of ker...
Abstract. We address the problem of incorporating transformation invariance in kernels for pattern analysis with kernel methods. We introduce a new class of kernels by so called Ha...
Abstract. The convex optimisation problem involved in fitting a kernel probit regression (KPR) model can be solved efficiently via an iteratively re-weighted least-squares (IRWLS)...
Kernel-based methods, e.g., support vector machine (SVM), produce high classification performances. However, the computation becomes time-consuming as the number of the vectors su...