一种基于样本加权的多尺度核支持向量机方法

多核学习方法是机器学习领域中的一个新的热点。核方法通过将数据映射到高维空间来增加线性分类器的计算能力,是目前解决非线性模式分析与分类问题的一种有效途径。但是在一些复杂的情况下,单个核函数构成的核学习方法并不能完全满足如数据异构或者不规则、样本规模大、样本分布不平坦等实际应用中的需求问题,因此将多个核函数进行组合以期获得更好的结果,是一种必然的发展趋势。因此提出一种基于样本加权的多尺度核支持向量机方法,通过不同尺度核函数对样本的拟合能力进行加权,从而得到基于样本加权的多尺度核支持向量机决策函数。通过在多个数据集上的实验分析可以得出所提方法对于各个数据集都获得了很高的分类准确率。

Novel Multi-scale Kernel SVM Method Based on Sample Weighting

Multi-kernel learning has been a new research focus in the current kernel machine learning field.Through mapping data into high dimensional space,kernel methods increase the computational power of linear classifier and they are an effective way to solve the problem of nonlinear model analysis and classification.In some complex situations,ne-vertheless,the kernel learning method of single kernel function can not completely satisfy the requirements of heterogeneous data or irregular data as well as samples of large size and non-flat distribution.Therefore,it is necessary to deve-lop multiple kernel functions in order to get better results.In this paper,we proposed a new SVM method for multi-scale kernel learning based on sample weighting,which is assigned via fitting abilities of distinct scales kernel functions for samples.Through the experimental analysis on several data sets,we can get that the method proposed in this paper can attain better classification accuracy on each data set.