Theodore B. Trafalis

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The main purpose of this paper is to compare the support vector machine (SVM) developed by Vapnik with other techniques such as Backpropagation and Radial Basis Function (RBF) Networks for financial forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP)(More)
Recent developments in computing and technology, along with the availability of large amounts of raw data, have contributed to the creation of many effective techniques and algorithms in the fields of pattern recognition and machine learning. The main objectives for developing these algorithms include identifying patterns within the available data or making(More)
This paper presents a reduced kernel-based classification model for multi-category discrimination of sets or objects. The proposed model is based on the Tikhonov regularization scheme. This approach extends Mangasarian reduced support vector machine (RSVM) model in a least square framework for the case of multi-categorical discrimination. The dimension(More)