Feature selection is fundamental to knowledge discovery from massive amount of high-dimensional data. In an effort to establish theoretical justification for feature selection al...
We study the computational complexity of some central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. O...
Tomas Brazdil, Vaclav Brozek, Kousha Etessami, Ant...
We present a new class of problems, called resource-bounded information gathering for correlation clustering. Our goal is to perform correlation clustering under circumstances in w...
Wireless IEEE 802.11 networks in residences, small businesses, and public “hot spots” typically encounter the wireline access link (DSL, cable modem, T1, etc.) as the slowest ...
Violeta Gambiroza, Bahareh Sadeghi, Edward W. Knig...
d at a high abstraction level, and consists in an expectation-driven search starting from symbolic object descriptions and using a version of a distributed blackboard system for re...
Gian Luca Foresti, Vittorio Murino, Carlo S. Regaz...