We present a principled Bayesian framework for modeling partial memberships of data points to clusters. Unlike a standard mixture model which assumes that each data point belongs ...
Katherine A. Heller, Sinead Williamson, Zoubin Gha...
In this paper we apply state-of-the-art approach to object detection and localisation by incorporating local descriptors and their spatial configuration into a generative probabil...
Joni-Kristian Kamarainen, Miroslav Hamouz, Josef K...
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
To successfully prepare and model data, the data miner needs to be aware of the properties of the data manifold. In this chapter, the outline of a tool for automatically generating...
Performance of distributed applications largely depends on the mapping of their components on the underlying architecture. On one mponent-based approaches provide an abstraction su...