摘要
针对UDEED算法中线性Logistic模型分类预测准确率较低的问题,基于泰勒展开式,提出一种多项式核的非线性Logistic模型改进算法。研究非线性Logistic模型的核函数参数估计方法,更新损失函数的计算规则,并利用梯度下降法求解改进UDEED模型,实现数据集的分类预测。实验结果表明,与UDEED算法相比,改进算法提高了分类预测的准确率。
To address the problem that the linear Logistic model in the UDEED algorithm has poor classification prediction accuracy,based on Taylor expansion,an improved nonlinear Logistic model algorithm for polynomial kernel is proposed.The estimation method for kernel function parameter of nonlinear Logistic model is studied,and the calculation rules of the loss function are updated.The improved UDEED model is solved by the gradient descent method,and the data set is classified and predicted.Experimental results show that compared with the UDEED algorithm,the improved algorithm improves the accuracy of classification prediction.
作者
庄立纯
张正军
张乃今
李君娣
ZHUANG Lichun;ZHANG Zhengjun;ZHANG Naijin;LI Jundi(School of Science,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第7期208-211,共4页
Computer Engineering
基金
国家自然科学基金(61773014)
