基于支撑向量置换核函数的一种领域知识与模型融合的技术

提出了一种修正撑向量核函数的理论与方法，与传统的方法相比，置换核函数的引入为领域知识与学习模型的融合提供了理论基础与方法。该文借助于置换的概念，对关于事物模式组成的不变性常识进行了形式化，求取了可以定量表述事物模式扰动的置换变换矩阵；在分类不变性的约束下，运用置换变换矩阵对核函数进行修正，获得了改进的学习模型，文本分类的实验表明，学习算法将文本领域内的知识有效地融合到了学习模型中，获得了更高的分辨率与泛化能力。

Fusion of Domain Knowledge and Model Based on Support Vector Permutation Kernel Function

This paper presents one theory and method for revising a support vector kernel function, by means of which "the permutation information" containing the invariance common sense is used in the process of SVM training. Compared with the traditional methods, the introduction of the permutation kernel function provides a theory foundation and methodology, which supports the fusion of knowledge and model. First, in terms of the conception of permutation, the invariance common sense about the structure of object is formalized, and the conceptions of the syngensis set and syngensis permutation are put forward; then, the permutation transformation matrix is solved, which expresses the disturbance of object pattern. Under the constraint of the classification invariance, the kernel function is revised using permutation transformation matrix. As a result, the SVM classifier based on permutation function is obtained. The experiment shows that the method in this paper is an effective one to improve the generalization performance of the SVM classifier with the permutation information. This paper also revises the sufficient condition needed by the classification invariance and proves it, which ensures the validity of the method.