Informatica

In this paper, we consider the problem of semi-supervised binary classification by Support Vector Machines (SVM). This problem is explored as an unconstrained and non-smooth optimization task when part of the available data is unlabelled. We apply non-smooth optimization techniques to classification where the objective function considered is non-convex and non-differentiable and so difficult to minimize. We explore and compare the properties of Simulated Annealing and of Simultaneous Perturbation Stochastic Approximation (SPSA) algorithms (SPSA with the Lipschitz Perturbation Operator, SPSA with the Uniform Perturbation Operator, Standard Finite Difference Approximation) for semi-supervised SVM classification. Numerical results are given, obtained by running the proposed methods on several standard test problems drawn from the binary classification literature. The performance of the classifiers were evaluated by analyzing Receiver Operating Characteristics (ROC).

The paper considers application of stochastic optimization to system of automatic recognition of ischemic stroke area on computed tomography (CT) images. The algorithm of recognition depends on five inputs that influence the results of automatic detection. The quality of recognition is measured by size of conjunction of ethalone image and the image calculated by the program of automatic detection. The method of Simultaneous Perturbation Stohastic Approximation algorithm with the Metropolis rule has been applied to the optimization of the quality of image recognition. The Monte-Carlo simulation experiment was performed in order to evaluate the properties of developed algorithm.