A kind of support vector fuzzy classifiers

Support vector machine (SVM) is a new promising machine learning method with good generalization ability, which learns the decision surface from two distinct classes of input points. But in many applications, the data are not always obtained precisely, i.e. there exist some fuzziness in the data. In this paper, we reformulated the conventional support vector classifiers such that they can learn from fuzzy input points given in the form of triangular fuzzy numbers.