A novel approach for comparing sequences of observations using an explicit-expansion kernel is demonstrated. The kernel is derived using the assumption of the independence of the ...
The existing methods for offline training of cascade classifiers take a greedy search to optimize individual classifiers in the cascade, leading inefficient overall performance. W...
The Hough transform provides an efficient way to detect objects. Various methods have been proposed to achieve discriminative learning of the Hough transform, but they have usuall...
Learning models for detecting and classifying object categories is a challenging problem in machine vision. While discriminative approaches to learning and classification have, in...
We consider a kernel-based approach to nonlinear classification that coordinates the generation of “synthetic” points (to be used in the kernel) with “chunking” (working wi...