We study the problem of PAC-learning Boolean functions with random attribute noise under the uniform distribution. We define a noisy distance measure for function classes and sho...
Nader H. Bshouty, Jeffrey C. Jackson, Christino Ta...
Recently in several papers, graphs with maximum neighborhood orderings were characterized and turned out to be algorithmically useful. This paper gives a unified framework for cha...
We present a analytical framework to identify the tradeoffs and performance impacts associated with different SoC platform configurations in the specific context of implementing m...
Alexander Maxiaguine, Yongxin Zhu, Samarjit Chakra...
We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept struc...
Computing invariants is the key issue in the analysis of infinite-state systems whether analysis means testing, verification or parameter synthesis. In particular, methods that all...
Saddek Bensalem, Marius Bozga, Jean-Claude Fernand...