We propose a low cost method for the correction of the output of OCR engines through the use of human labor. The method employs an error estimator neural network that learns to as...
We extend qualitative reasoning with estimations of the relative likelihoods of the pos sible qualitative behaviors . We estimate the likelihoods by viewing the dynamics o f a sys...
Set-valued estimation offers a way to account for imprecise knowledge of the prior distribution of a Bayesian statistical inference problem. The set-valued Kalman filter, which p...
This paper proposes a learning scheme based still image super-resolution reconstruction algorithm. Superresolution reconstruction is proposed as a binary classification problem an...
We study the empirical meaning of randomness with respect to a family of probability distributions P, where is a real parameter, using algorithmic randomness theory. In the case w...