We propose a novel approach that reduces cost-sensitive classification to one-sided regression. The approach stores the cost information in the regression labels and encodes the m...
Kernel-based methods, e.g., support vector machine (SVM), produce high classification performances. However, the computation becomes time-consuming as the number of the vectors su...
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal ...
In this paper we discuss object detection when only a small number of training examples are given. Specifically, we show how to incorporate a simple prior on the distribution of n...