基于邻域原理计算海量数据支持向量的研究

Study on Computing the Support Vectors of Massive Data Based on Neighborhood Principle

使用支持向量机理论计算海量数据的支持向量是相当困难的 .为了解决这个问题 ,提出了基于邻域原理计算支持向量的方法 .在对支持向量机原理与邻域原理比较分析的基础上讨论了以下问题 :(1)构建了从样本空间经过特征空间到扩维空间的复合内积函数 ,给出计算支持向量的邻域思想 ;(2 )将支持向量机的理论建立在距离空间上 ,设计出了计算支持向量的邻域算法 ,从而把该算法理解为简化计算二次规划的方法 ;(3)实验结果说明 ,邻域原理可以有效地解决对海量数据计算支持向量的问题 .

It is quite difficult to compute the support vectors of massive data using the theory of support vector machine. To solve this problem, a method is brought forward to compute support vectors based on the neighborhood principle. Several questions are discussed based upon comparison and analysis of the support vector machine theory and the neighborhood principle as below: (1) The inner product function from the sample space to the dimension-expand space via the feature space is constructed, and the neighborhood principle of computing the support vectors is presented; (2) Vapnik's support vector machine theory is constructed on the distance space, the algorithm is designed to compute support vectors, and the algorithm is regarded as a method to reduce the computation of quadratic programming; (3) The experimental results show that the neighborhood principle can solve the problem of support vector computation of massive data effectively.