A New Formulation for Classification by Ellipsoids

We propose a new formulation for the optimal separation problems. This robust formulation is based on finding the minimum volume ellipsoid covering the points belong to the class. Idea is to separate by ellipsoids in the input space without mapping data to a high dimensional feature space unlike Support Vector Machines. Thus the distance order in the input space is preserved. Hopfield Neural Network is described for solving the optimization problem. The benchmark Iris data is given to evaluate the formulation

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with a flexible access to the information allowing them for writing a query in natural language and obtaining a concise answer. QA systems are mainly suited to English as the target language. In this paper we will investigate how much the translation of the queries, from the Arabic into the English language, could reduce the accuracy of the QA task. 1

S.: Fast pattern selection for support vector classifiers, Lecture

Abstract. Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. Preliminary simulation results were promising: Up to two orders of magnitude, training time reduction was achieved including the preprocessing, without any loss in classification accuracies.