The recurrent network of Xia et al. (1996) was proposed for solving quadratic programming problems and was recently adapted to support vector machine (SVM) learning by Tan et al. (2000). We show that this formulation contains some unnecessary circuits which, furthermore, can fail to provide the correct value of one of the SVM parameters and suggest how to avoid these drawbacks.

This paper presents a novel algorithm for density estimation which is based on the support vector machines (SVM) approach and it uses the Mean Field (MF) theory for developing an easy and efficient learning procedure for the SVM. The traditional formulation of the SVM density estimation decomposes the parameters of the problem into a quadratic optimization which can be solved using standard opt...

The Brain Computer Interface (BCI) has a great impact on mankind. Many researchers have been trying to employ different classifiers figure out the human brain's thoughts accurately. In order overcome poor performance of single classifier, some used combined classifier. Others delete redundant information in channels before applying classifier as they thought it might reduce accuracy BCI helps c...

In this paper, we present a new method for solving quadratic programming problems, not strictly convex. Constraints of the problem are linear equalities and inequalities, with bounded variables. The suggested method combines the active-set strategies and support methods. The algorithm of the method and numerical experiments are presented, while comparing our approach with the active set method ...

We investigate the use of interior-point methods for solving quadratic programming problems with a small number of linear constraints, where the quadratic term consists of a low-rank update to a positive semidefinite matrix. Several formulations of the support vector machine fit into this category. An interesting feature of these particular problems is the volume of data, which can lead to quad...

We derive multiplicative updates for solving the nonnegative quadratic programming problem in support vector machines (SVMs). The updates have a simple closed form, and we prove that they converge monotonically to the solution of the maximum margin hyperplane. The updates optimize the traditionally proposed objective function for SVMs. They do not involve any heuristics such as choosing a learn...

In sensitivity analysis one wants to know how the problem and the optimal solutions change under the variation of the input data. We consider the case when variation happens in the right hand side of the constraints and/or in the linear term of the objective function. We are interested to find the range of the parameter variation in Convex Quadratic Optimization (CQO) problems where the support...

In support set expansion sensitivity analysis, one concerns to find the range of parameter variation where the perturbed problem has an optimal solution with the support set that includes the support set of the given optimal solution of the unperturbed problem. In this paper, we consider the perturbed convex quadratic optimization problem and present a method to identify the support set expansi...

in cite{p}, park introduced the quadratic $rho$-functional inequalitiesbegin{eqnarray}&& |f(x+y)+f(x-y)-2f(x)-2f(y)| && qquad le  left|rholeft(2 fleft(frac{x+y}{2}right) + 2 fleft(frac{x-y}{2}right)- f(x) -  f(y)right)right|,  nonumberend{eqnarray}where $rho$ is a fixed complex number with $|rho|andbegin{eqnarray}&& left|2 fleft(frac{x+y}{2}right) + 2 fleft(frac{x-y}{2}r...