A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearl...
We present a general algorithm of image based regression that is applicable to many vision problems. The proposed regressor that targets a multiple-output setting is learned using...
Shaohua Kevin Zhou, Bogdan Georgescu, Xiang Sean Z...
We develop the notion of normalized information distance (NID) [7] into a kernel distance suitable for use with a Support Vector Machine classifier, and demonstrate its use for an...
A novel nonlinear discriminant analysis method, Kernelized Decision Boundary Analysis (KDBA), is proposed in our paper, whose Decision Boundary feature vectors are the normal vecto...
: A novel support vector machine method for classification is presented in this paper. A modified kernel function based on the similarity metric and Riemannian metric is applied ...