In this paper a novel and generic approach for model-based data clustering in a boosting framework is presented. This method uses the forward stagewise additive modeling to learn t...
This paper presents a technique to learn dynamic appearance models from a small number of training frames. Under this framework, dynamic appearance is modelled as an unknown opera...
We address the issue of compiling ML pattern matching to compact and efficient decisions trees. Traditionally, compilation to decision trees is optimized by (1) implementing decis...
Many applications in text and speech processing require the analysis of distributions of variable-length sequences. We recently introduced a general kernel framework, rational ker...
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...