In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive proced...
Abstract—In this work we present a variational formulation for a multilayer perceptron neural network. With this formulation any learning task for the neural network is defined ...
Traditionally, in-network services like firewall, proxy, cache, and transcoders have been provided by dedicated hardware middleboxes. A recent trend has been to remove the middleb...
Jeongkeun Lee, Jean Tourrilhes, Puneet Sharma, Suj...
We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a...
We present a sophisticated framework to systematically explore the temporal correlation in environmental monitoring wireless sensor networks. The presented framework optimizes los...