穆晓霞, 陈留院. 自适应双正则化支持向量机的解路算法[J]. 信阳师范学院学报(自然科学版), 2014, 27(2): 288-291. DOI: 10.3969/j.issn.1003-0972.2014.02.031
引用本文: 穆晓霞, 陈留院. 自适应双正则化支持向量机的解路算法[J]. 信阳师范学院学报(自然科学版), 2014, 27(2): 288-291. DOI: 10.3969/j.issn.1003-0972.2014.02.031
Mu Xiaoxia , Chen Liuyuan . Solution Path Algorithm for the Doubly Regularized Support Vector Machine[J]. Journal of Xinyang Normal University (Natural Science Edition), 2014, 27(2): 288-291. DOI: 10.3969/j.issn.1003-0972.2014.02.031
Citation: Mu Xiaoxia , Chen Liuyuan . Solution Path Algorithm for the Doubly Regularized Support Vector Machine[J]. Journal of Xinyang Normal University (Natural Science Edition), 2014, 27(2): 288-291. DOI: 10.3969/j.issn.1003-0972.2014.02.031

自适应双正则化支持向量机的解路算法

Solution Path Algorithm for the Doubly Regularized Support Vector Machine

  • 摘要: 针对自适应双正则化支持向量机,证明了其最优解关于单正则化参数是分段线性的,并据此提出了完全正则化解路算法,最后,通过在急性白血病数据集上进行分类实验,验证了所提算法的有效性

     

    Abstract: For the adaptive doubly regularized support vector machine, it was proved that its solution path is piecewise linear with respect to one regularized parameter when another parameter is fixed. Based on this result, an entire regularized solving path algorithm was proposed. The experiment results on the binary classification of acute leukemia demonstrated the effectiveness of the proposed algorithm.

     

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