We develop improved risk bounds for function estimation with models such as single hidden layer neural nets, using a penalized least squares criterion to select the size of the mod...
In this paper, we present the results of an experimental comparison among seven different weight initialization methods in twelve different problems. The comparison is performed by...
We investigate the use of an unsupervised artificial neural network to form a sparse representation of the underlying causes in a data set. By using fixed lateral connections that...
Neural networks use neurons of the same type in each layer but such architecture cannot lead to data models of optimal complexity and accuracy. Networks with architectures (number ...
This paper describes a method of supervised learning based on forward selection branching. This method improves fault tolerance by means of combining information related to general...