We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert M...
A structural similarity kernel is presented in this paper for SVM learning, especially for learning with imbalanced datasets. Kernels in SVM are usually pairwise, comparing the sim...
This work aims at defining an extension of a competitive method for matching correspondences in stereoscopic image analysis. The method we extended was proposed by Venkatesh, Y.V....
We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel Criterion. In addition, an eff...
Shu Yang, Shuicheng Yan, Dong Xu, Xiaoou Tang, Cha...
This paper presents a new approach to ranking relevant images for retrieval. Distance in the feature space associated with a kernel is used to rank relevant images. An adaptive qu...