A New SVM Model for Classifying Genetic Data: Method, Analysis and Results
نویسندگان
چکیده
In this paper we present a new formulation of the Support Vector Machine for classifying data. It is based on development of ideas from methods of total least squares, in which error in measured data is incorporated in the model design. The new formulation studied is similar to the soft margin SVM, but has to be solved using nonlinear optimization rather than quadratic programming. Initial results are discussed demonstrating its robustness for classification of data with a large feature space.
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