This paper extends previous work on the Skewing algorithm, a promising approach that allows greedy decision tree induction algorithms to handle problematic functions such as parit...
We consider a fundamental problem in computational learning theory: learning an arbitrary Boolean function which depends on an unknown set of k out of n Boolean variables. We give...
Elchanan Mossel, Ryan O'Donnell, Rocco A. Servedio
— In this paper, we approach the gate sizing problem in VLSI circuits in the context of increasing variability of process and circuit parameters as technology scales into the nan...
In this paper, we propose a probabilistic method to model the dynamic traffic flow across nonoverlapping camera views. By assuming the transition time of object movement follows a...
In this paper we propose a new tabu search hyperheuristic which makes individual low level heuristics tabu dynamically using an analogy with the Binary Exponential Back Off (BEBO) ...
Stephen Remde, Keshav P. Dahal, Peter I. Cowling, ...