This paper addresses the problem of classification in situations where the data distribution is not homogeneous: Data instances might come from different locations or times, and t...
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medic...
Polina Golland, W. Eric L. Grimson, Martha Elizabe...
Background: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled...
This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a ...