Improved UDEED Algorithm Based on Nonlinear Logistic Model

doi:10.19678/j.issn.1000-3428.0050800

Computer Engineering ›› 2019, Vol. 45 ›› Issue (7): 208-211. doi: 10.19678/j.issn.1000-3428.0050800

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Improved UDEED Algorithm Based on Nonlinear Logistic Model

ZHUANG Lichun, ZHANG Zhengjun, ZHANG Naijin, LI Jundi

School of Science, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2018-03-15 Revised:2018-05-04 Online:2019-07-15 Published:2019-07-23

基于非线性Logistic模型的改进UDEED算法

庄立纯, 张正军, 张乃今, 李君娣

南京理工大学理学院, 南京 210094

作者简介:庄立纯(1994-),女,硕士研究生,主研方向为数据挖掘;张正军(通信作者),副教授、博士;张乃今、李君娣,硕士研究生。
基金资助:
国家自然科学基金（61773014）。

Abstract

Abstract: 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.

Key words: UDEED algorithm, nonlinear Logistic model, semi-supervised learning, unlabeled data, gradient descent

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。

关键词: UDEED算法, 非线性Logistic模型, 半监督学习, 无标签数据, 梯度下降

CLC Number:

TP301.6

ZHUANG Lichun, ZHANG Zhengjun, ZHANG Naijin, LI Jundi. Improved UDEED Algorithm Based on Nonlinear Logistic Model[J]. Computer Engineering, 2019, 45(7): 208-211.

庄立纯, 张正军, 张乃今, 李君娣. 基于非线性Logistic模型的改进UDEED算法[J]. 计算机工程, 2019, 45(7): 208-211.

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http://www.ecice06.com/EN/Y2019/V45/I7/208

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