Space constrained optimization problems arise in a multitude of important applications such as data warehouses and pervasive computing. A typical instance of such problems is to s...
Themistoklis Palpanas, Nick Koudas, Alberto O. Men...
We propose a well-founded method of ranking a pool of m trained classifiers by their suitability for the current input of n instances. It can be used when dynamically selecting a s...
In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a lo...
Vincenzo Cutello, Giuseppe Nicosia, Mario Pavone, ...
With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the te...
In this paper, we study the average case complexity of the Unique Games problem. We propose a natural semi-random model, in which a unique game instance is generated in several st...
Alexandra Kolla, Konstantin Makarychev, Yury Makar...