We consider the problem of numerical stability and model density growth when training a sparse linear model from massive data. We focus on scalable algorithms that optimize certain...
In this paper we focus on high dimensional data sets for which the number of dimensions is an order of magnitude higher than the number of objects. From a classifier design standp...
A class of sparse regularization functions are considered for the developing sparse classifiers for determining facial gender. The sparse classification method aims to both select...
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The ad...
Recently we presented a new approach [20] to the classification problem arising in data mining. It is based on the regularization network approach but in contrast to other methods...