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The Study of Support Vector Machine to Classify the Medical Data


Ghasem Farjamnia, Mehdi Zekriyapanah Gashti, Hamed Barangi, Yusif S. Gasimov


Vol. 17  No. 12  pp. 145-150


In this article, we are going to study the linear support vectors and their performance in the related classification issues. Using the linear support vectors (SVM's) in the classification issues is a new approach that in recent years is considered by many scientists. It was used in a wide range of applications including OCR, Handwriting recognition, guidance signs diagnosis and etc. SVM approach is in a way that in the training phase, it is tried to choose the limit of decision-making (Decision Boundary) is such a way that its minimum distance to each of the considered categories stays maximum. This kind of choice helps our decision in practice to tolerate the noisy condition very well and has a good response. This way of selecting the boundary is based on the points that are named as support vectors. At first we study the concepts such as generalization of a pattern recognition machine and then the VC dimension that has a great application in the concept of classification machines. And then we describe the linear and non-linear support vectors and Kernel functions. And eventually, we will study the VC dimension for some of these functions.


Support Vector Machine, VC Dimension, Mercer, Linear SVM, Nonlinear SVM RBF Kernel, Medical Data