Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of l...
This paper proposes a new single-frame image upconversion approach that uses prior information. The proposed method overcomes the drawbacks of the Kondo 2001 where the class membe...
Using a previously developed automated method for enzyme annotation, we report the reannotation of the ENZYME database and the analysis of local error rates per class. In control ...
Benjamin Audit, Emmanuel D. Levy, Walter R. Gilks,...
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
—We present a framework for analyzing the shape deformation of structures within the human brain. A mathematical model is developed describing the deformation of any brain struct...
John Martin, Alex Pentland, Stan Sclaroff, Ron Kik...