One of the main research concern in neural networks is to find the appropriate network size in order to minimize the trade-off between overfitting and poor approximation. In this ...
In this paper we present a non parametric density-based data reduction technique designed to be used in robust parameter estimation problems. Existing approaches are focused on red...
Density estimation with Gaussian Mixture Models is a popular generative technique used also for clustering. We develop a framework to incorporate side information in the form of e...
Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
: Relational representation of objects using graphs reveals much information that cannot be obtained by attribute value representations alone. There are already many databases that...