Abstract. In this paper we introduce a new approach to automatic attribute and granularity selection for building optimum regression trees. The method is based on the minimum descr...
: Because of the semantic gap between low-level feature and high-level semantic feature of images, the results of the traditional color-based image retrieval can't meet users&...
Supervised methods for extractive speech summarization require a large training set. Summary annotation is often expensive and time consuming. In this paper, we exploit semisuperv...
Abstract—Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work ar...
Ulf Blanke, Bernt Schiele, Matthias Kreil, Paul Lu...
The problem of automatic feature selection/weighting in kernel methods is examined. We work on a formulation that optimizes both the weights of features and the parameters of the ...