Sufficient number of linear and noisy measurements for exact and approximate sparsity pattern/support set recovery in the high dimensional setting is derived. Although this problem as been addressed in the recent literature, there is still considerable gaps between those results and the exact limits of the perfect support set recovery. To reduce this gap, in this paper, the sufficient con...

‎Many optimization problems can be reduced to a problem with an increasing and co-radiant objective function by a suitable transformation of variables. Functions, which are increasing and co-radiant, have found many applications in microeconomic analysis. In this paper, the abstract convexity of positive valued affine increasing and co-radiant (ICR) functions are discussed. Moreover, the ...

‎a total dominating set of a graph $g$ is a set $d$ of vertices of $g$ such that every vertex of $g$ has a neighbor in $d$‎. ‎the total domination number of a graph $g$‎, ‎denoted by $gamma_t(g)$‎, ‎is~the minimum cardinality of a total dominating set of $g$‎. ‎chellali and haynes [total and paired-domination numbers of a tree, akce international ournal of graphs and combinatorics 1 (2004)‎, ‎6...

decision support systems (dsss) are prevalent information systems for decision making in many competitive business environments. in a dss, decision making process is intimately related to some factors which determine the quality of information systems and their related products. traditional approaches to data analysis usually cannot be implemented in sophisticated companies, where managers need...

decision support systems (dsss) are prevalent information systems for decision making in many competitive business environments. in a dss, decision making process is intimately related to some factors which determine the quality of information systems and their related products. traditional approaches to data analysis usually cannot be implemented in sophisticated companies, where managers nee...

Actively sampled data can have very different characteristics than passively sampled data. Therefore, it’s promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for imbalanced data, a situation that arises for many HLT tasks. The key idea behin...

In our previous work we have discussed the training method of a support vector classifier by active set training allowing the solution to be infeasible during training. In this paper, we extend this method to training a support vector regressor (SVR). We use the dual form of the SVR where variables take real values and in the objective function the weighted linear sum of absolute values of the ...