, 2001)

The use of support vector machine learning is widely supported to be used to notice micro calcification clusters in the digital mammograms. It is indeed a learning tool that originated from modern statistical theory of learning. (Vapnik, 1998). In the recent past years, SVM learning has got a large range of real life applications. This includes handwritten digit detection (Scholkopf et al., 1997), recognition of object, (Pontil&Verri, 1998), identification of speaker (Wan&Campbell, 2000) and detection of face in images,(Osuna et al.,1997) . Categorization of text is done by SVM. (Joachims,1999). SVM learning formulation has its basis on structural risk minimization principle. It does not minimize an object function on the basis of training examples but on the contrary, SVM tries to minimize leap on generalization error. This is usually the error that is done by the learning machine on the test data that is not used while undertaking...
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