there are different ideas and opinions about the effects of macroeconomic variables on real and nominal variables. to answer the question of whether changes in macroeconomic variables as a political tool is useful over a business cycle, understanding the effect of macroeconomic variables on economic growth is important. in the present study, the bayesian vector autoregresive model and seasonali...

An algorithm for fitting the entire regularization path of the support vector machine (SVM) was recently proposed by Hastie et al. (2004). It allows effective computation of solutions and greatly facilitates the choice of the regularization parameter that balances a trade-off between complexity of a solution and its fit to data. Extending the idea to more general setting of the multiclass case,...

The concept of comonotonicity is a useful tool for solving various problems in insurance and financial economics. The credibilistic comonotonicity of fuzzy vector is defined via its comonotonic support. In general case, the properties of comonotonic fuzzy vector are discussed based on the relation between the joint monotone distribution of a fuzzy vector and its marginal monotone distributions....

A two-class Support Vector Machine (SVM) classifier finds a hyperplane that separates two classes of data with the maximum margin. In a first learning phase, SVM involves the construction and solution of a primal-dual interiorpoint optimization problem. Each iteration of the interior-point method (IPM) requires a sparse linear system solution, which dominates the execution time of SVM learning....

In this paper, we study the classical sensitivity analysis when the right - hand – side vector, and the coefficients of the objective function are allowed to vary. 

porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. nowadays, using intelligent techniques has become a popular method for porosity estimation. support vector machine (svm) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced fo...

A finite concave minimization algorithm is proposed for constructing kernel classifiers that use a minimal number of data points both in generating and characterizing a classifier. The algorithm is theoretically justified on the basis of linear programming perturbation theory and a leave-one-out error bound as well as effective computational results on seven real world datasets. A nonlinear rec...

The quantization of the most general Bianchi Type II geometry –with all six scale factors, as well as the lapse function and the shift vector, present– is considered. In an earlier work, a first reduction of the initial 6-dimensional configuration space, to a 4-dimensional one, has been achieved by the usage of the information furnished by the quantum form of the linear constraints. Further red...

This article deals with the design of quadratic and linear dynamic output feedback controllers for systems to ensure, on one hand, local exponential stability origin in closed-loop form and, other increase an estimate basin attraction as large possible. The introduction a term controller allows us consider same class dynamics studied system. Here, our approach is Lyapunov function respect exten...