In this paper, we study the problem of learning block classification models to estimate block functions. We distinguish general models, which are learned across multiple sites, an...
A novel procedure for segmenting a set of scattered 3D data obtained from a head and shoulders multiview sequence is presented. The procedure consists of two steps. In the first ...
Abstract— This paper reports a novel method for fully automated segmentation that is based on description of shape and its variation using point distribution models (PDM’s). An...
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an ex...