Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
The goal of the MKRP project is the development of a theorem prover which can be used as an inference engine in various applications, in particular it should be capable of proving ...
A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, ...
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapir...
In this paper, we show how adaptive prototype optimization can be used to improve the performance of function approximation based on Kanerva Coding when solving largescale instanc...
Computational grids offer users a simple access to tremendous computer resources for solving large scale computing problems. Traditional performance analysis of scheduling algorit...