In this paper we first overview the main concepts of Statistical Learning Theory, a framework in which learning from examples can be studied in a principled way. We then briefly di...
Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers (Gao et al., 2008) and (Wang et al., 2007). This paper is co...
Abstract. We propose a method of unsupervised learning from stationary, vector-valued processes. A low-dimensional subspace is selected on the basis of a criterion which rewards da...
Abstract - We discuss an ensemble-of-classifiers based algorithm for the missing feature problem. The proposed approach is inspired in part by the random subspace method, and in pa...
This work considers the mini-slot scheduling problem in IEEE 802.16d wireless mesh networks (WMNs). An efficient mini-slot scheduling needs to take into account the transmission o...