Support Vector Machine Based on Boundary Vectors Adjustable Entropy Function

When the size of the training set is large, learning process in general support vector machines take a lot of memory, optimizing slow, is not conducive to practical application. This paper presents a boundary vector-based SVM adjustable entropy function method. First, we use two methods convex hull boundary vectors relative pre-extracted boundary vectors; Then, to train pre-drawn boundary vectors in SVM adjustable entropy method. Experiments show that this method not only reduces the training sample set the price of learning, but also improve the classification rate.