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信息技术2018 年3 期

基于L2 正则化的逻辑回归求解设计
黄雄鹏
(贵州师范大学国际教育学院,贵州 贵阳 550001)

摘  要:本文通过对L2 正则化逻辑回归进行分析,使用随机梯度下降(SGD)和限制内存拟牛顿法(L-BFGS)来求解回归参数使得条件对数似然函数最大。在手写数字图像数据集USPS-N 和HTML 网页数据集上的两分类结果表明,随机梯度下降求解方法在两数据集上有较高的测试错误率。因此,在设计L2 正则化逻辑回归求解方法时,可使用限制内存拟牛顿法作为缺省求解方法。


关键词:逻辑回归;随机梯度下降法;限制内存拟牛顿法



中图分类号:TP302.2         文献标识码:A         文章编号:2096-4706(2018)03-0016-02


Design of Logical Regression Solving Based on L2 Regularization
HUANG Xiongpeng
(School of International Education,Guizhou Normal University,Guiyang 550001,China)

Abstract:By analyzing the L2 regularized logistic regression,this paper uses random gradient descent (SGD) and limitedmemory quasi Newton method (L-BFGS) to solve the regression parameter to make the maximum of the conditional log likelihood function. The two classification results on the handwritten digital image data set USPS-N and the HTML web data set show that the random gradient descent method has a higher test error rate on the two data set. Therefore,when designing L2 regularized logistic regression method,the restricted memory quasi Newton method can be used as the default solution.

Keywords:logical regression;stochastic gradient descent method;restricted memory quasi Newton method


参考文献:

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作者简介:黄雄鹏(1994-),男,侗族,贵州余庆人。研究方向:计算机科学与技术。