We propose a new binary classification and variable selection technique especially designed for high dimensional predictors. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection along with classification. By adding an `1-type pena...

In this paper, the outage performance is investigated for cognitive dual-hop relay networks with amplify-and-forward (AF) relay and selection combining (SC) receiver at the destination under spectrum sharing constraints on primary user (PU) over independent and identically distributed (i.i.d.) Nakagami-m fading channels. We derive an exact closed-form expression for the outage probability (OP) ...

Recently, forward greedy selection method has been successfully applied to approximately solve sparse learning problems, characterized by a trade-off between sparsity and accuracy. In this paper, we generalize this method to the setup of sparse approximation over a pre-fixed dictionary. A fully corrective forward selection algorithm is proposed along with convergence analysis. The periteration ...

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X(1), X(2), …, X(p) through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such ...

We propose a new variable selection and estimation technique for high dimensional single index model with unknown monotone smooth link function. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection. In this article, we propose a n...

biomedical datasets usually include a large number of features relative to the number of samples. however, some data dimensions may be less relevant or even irrelevant to the output class. selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. to this end, this paper presents a hybrid method of filter and wr...

Forward Checking is a highly regarded search method used to solve Constraint Satisfaction Problems. This method performs a limited type of lookahead attempting to nd a failure earlier during a backtracking search. In this paper a new search method, Minimal Forward Checking, is introduced which under certain conditions performs the same amount of constraint checking in the worst case as Forward ...

On the basis of the polyclonal selection algorithm, an improved polyclonal selection algorithm based on negative selection is put forward in this paper. Inspired by the diversity and auto-tolerance of the negative selection, the population diversity is increased by the clone deletion and clone supply to improve the polyclonal selection algorithm. Finally, the optimization design of the crane bo...

We introduce a Forward Backward and Model Selection algorithm (FBMS) for constructing a hybrid regression network of radial and perceptron hidden units. The algorithm determines whether a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determines the number of hidden units. Then the algorithm uses model selection criteria and p...

The performance of fast forward-backward splitting, or equivalently fast proximal gradient methods, depends on the conditioning of the optimization problem data. This conditioning is related to a metric that is defined by the space on which the optimization problem is stated; selecting a space on which the optimization data is better conditioned improves the performance of the algorithm. In thi...