摘要
标准的SVM分类计算过程中有大量的支持向量参与了计算,导致了分类速度缓慢。该文为提高SVM的分类速度,提出了一种快速的多项式核函数SVM分类算法,即将使用多项式核的SVM分类决策函数展开为关于待分类向量各分量的多项式,分类时通过计算各个多项式的值而得到分类结果,使分类计算量和支持向量数量无关,又保留了全部支持向量的信息。当多项式核函数的阶数或待分类向量的维数较低而支持向量数量较多时,使用该算法可以使SVM分类的速度得到极大的提高。针对实际数据集的实验表明了该算法的有效性。
When the number of support vectors is large, the classification speed of a kernel function based on support vectors classifier is inevitably very slow in test phase, as it need to perform the computation between each support vector and the classified vector. To address this, a fast classification algorithm for polynomial kernel support vector machines is presented, which expands the decision function of SVM into polynomials, and classifies new patterns by calculating the polynomials＇ value. The computational requirement of the algorithm is independent of the number of the support vectors, while the solution otherwise is unchanged. When the degree of the polynomial kernel or the dimension of the input space is small, the classification speed of this algorithm is much faster than the standard SVM classification method. The efficiency of this algorithm is also verified by the experiment result with real-world data set.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第6期27-29,32,共4页
Computer Engineering
基金
国家自然科学基金资助项目(60675019)
关键词
支持向量机
多项式
分类
Support vector machines
Polynomial
Classification
