A Support Vector Classifier Based on Vague Similarity Measure

Abstract EN

Support vector machine (SVM) is a popular machine learning method for its high generalizaiton ability.

How to find the adaptive kernel function is a key problem to SVM from theory to practical applications.

This paper proposes a support vector classifer based on vague sigmoid kernel and its similarity measure.

The proposed method uses the characteristic of vague set, and replaces the traditional inner product with vague similarity measure between training samples.

The experimental results show that the proposed method can reduce the CPU time and maintain the classification accuracy.