Graph structures have been proved important in high level-vision since they can be used to represent structural and relational arrangements of objects in a scene. One of the probl...
This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construc...
A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the appr...
Huyen Do, Alexandros Kalousis, Adam Woznica, Melan...
Abstract. During recent years much effort has been spent in incorporating problem specific a-priori knowledge into kernel methods for machine learning. A common example is a-prior...
Sparse kernel regressors have become popular by applying the support vector method to regression problems. Although this approach has been shown to exhibit excellent generalization...