We present a new model for studying mul-titask learning, linking theoretical results to practical simulations. In our model all tasks are combined in a single feedforward neu-ral network. Learning is implemented in a Bayesian fashion. In this Bayesian framework the hidden-to-output weights, being speciic to each task, play the role of model parameters. The input-to-hidden weights, which are sha...

Using naive Bayes for email classification has become very popular within the last few months. They are quite easy to implement and very efficient. In this paper we want to present empirical results of email classification using a combination of naive Bayes and k-nearest neighbor searches. Using this technique we show that the accuracy of a Bayes filter can be improved slightly for a high numbe...

Constrained Bayes methodology represents an alternative to the posterior mean (empirical Bayes) method commonly used to produce random effect predictions under mixed linear models. The general constrained Bayes methodology of Ghosh (1992) is compared to a direct implementation of constraints, and it is suggested that the former approach could feasibly be incorporated into commercial mixed model...

The empirical Bayes estimator is applied to software failures production. The time between failures data registered up to a given time, are used in order to estimate the probability of failure appearance during the next interval time. This method is similar to the estimation of n-grams in natural language processing. A modified expression to the estimator usually used in language and speech pro...