Research on Kernel Function of Support Vector Machine
首发时间:2013-07-08
Abstract:Support Vector Machine is a kind of algorithm used for classifying linear and nonlinear data, which not only has a solid theoretical foundation, but is more accurate than other sorting algorithms in many areas of applications, especially in dealing with high-dimensional data. It is not necessary for us to get the specific mapping function in solving quadratic optimization problem of SVM, and the only thing we need to do is to use kernel function to replace the complicated calculation of the dot product of the data set, reducing the number of dimension calculation. This paper introduces the theoretical basis of support vector machine, summarizes the research status and analyses the research direction and development prospects of kernel function.
keywords: support vector machine high-dimension data kernel function quadratic optimization
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支持向量机的核函数研究
摘要:支持向量机是一种可以对线性和非线性数据进行分类的算法,它不仅具有扎实的理论基础,而且在许多领域的应用中(尤其在处理高维数据时)比其他大多数分类算法更准确。在求解支持向量机的二次优化问题时,利用核函数代替数据集点积的复杂运算,不仅可以减少计算维度的数量,我们甚至可以不必知道具体的映射函数。本文介绍了支持向量机研究的理论基础,综述了核函数的研究现状,并对其研究方向和发展前景进行了分析。
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