一种新的核化SVM多层分类方法

利用核化思想提出了一种新的SVM多层分类算法。该算法的基本思路是：先利用Mercer核，将输入空间非线性可分的训练样本映射到高维特征空间Hilbert中，使之线性可分，然后采用最小超球体类包含作为层次分类的依据来生成二叉决策树，从而实现在高维空间中的多类分类。实验表明，采用该算法进行多类分类，可以有效地解决输入空间非线性可分问题，并可在一定程度上提高分类器的分类精度。

Improved algorithm for kernel-based SVM

This paper proposes a new SVM multi-classification method utilizing the kernel theory.To get better separability, the input space is mapped to a high-dimensional feature space (Hilbert) applying Mercer kernel function.With a suitable choice of the kernel,the data can become separable in feature space despite being non-separable in the original input space.Then the hy-persphere class least cover is used to be the rules of constructing binary tree.Classification experiments prove that the improved algorithm has better classifying performance than the other methods,and resolve the problem of nonlinear separability of classification in input space effectively.