We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
Abstract. This paper summarizes some of the current research challenges arising from multi-channel sequence processing. Indeed, multiple real life applications involve simultaneous...
Abstract. We present an SVM-based learning algorithm for information extraction, including experiments on the influence of different algorithm settings. Our approach needs fewer ...
We present DiPerF, a distributed performance-testing framework, aimed at simplifying and automating service performance evaluation. DiPerF coordinates a pool of machines that test...
Catalin Dumitrescu, Ioan Raicu, Matei Ripeanu, Ian...
Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this...