基于分形插值核函数的支持向量机算法

SUPPORT VECTOR MACHINE ALGORITHM BASED ON FRACTAL INTERPOLATION KERNEL FUNCTION

由于使用内积方式不能真实地表达数据间关系,造成分类效果参差不齐,故提出一种基于分形插值的支持向量机核函数算法。对样本数据进行预处理使样本数据范数在0~1之间,利用二次范数计算训练样本间距离关系并通过0-1原则区分同类与异类数据。通过计算数据间距离对新数据进行排序,建立同异类标签的区分最小最大区分距离,同异类标签交叉空间利用分形插值方法建立迭代系统与分形插值函数。实验结果表明,该算法能够有效增强交叉空间的区分度,缩短同类空间的差异性,进而达到提高分类准确率的效果。

Since the way of inner product can not express the true relationship between data,resulting in classification mixed results.This paper proposes a support vector machine kernel function algorithm based on fractal interpolation.It preprocessed the sample data so that the sample data norm was between 0 and 1,then used the quadratic norm to calculate the distance relationship between the training samples and distinguished the similar and heterogeneous data by the 0-1 principle.It calculated the data between the data,and the new data was sorted by distance.We established the minimum and maximum distinguishing distance of the same-class label,and we used the fractal interpolation method to establish the iterative system and the fractal interpolation function.The experimental results show that the proposed algorithm can effectively enhance the discrimination of cross-space and shorten the difference of similar space,so as to improve the accuracy of classification.