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...
Wepresent a novel, fast methodfor associationminingill high-dimensionaldatasets. OurCoincidence Detection method, which combines random sampling and Chernoff-Hoeffding bounds with...
Programmable logic architectures increase in capacity before commercial circuits are designed for them, yielding a distinct problem for FPGA vendors: how to test and evaluate the ...
Michael D. Hutton, Jonathan Rose, Derek G. Corneil
Language learning from positive data in the Gold model of inductive inference is investigated in a setting where the data can be modeled as a stochastic process. Specifically, the...
Abstract. We present Shekoosh, a novel framework for constraint-based generation of structurally complex inputs of large sizes. Given a Java predicate that represents the desired s...