大规模机器学习的优化方法

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI:10.1109/ICCWAMTIP53232.2021.9674150

Yuan Gao, Jianping Li, Yue Zhou, Fei Xiao, He Liu

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引用次数: 1

摘要

本文主要通过逻辑回归完成了RCV1文本数据集的二值分类。在建立logistic回归模型的基础上，研究了随机梯度下降、小批量随机梯度和L-BFGS三种数值优化算法的性能和特点。

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Optimization Methods For Large-Scale Machine Learning

This paper mainly completes the binary classification of RCV1 text data set by logistic regression. Based on the established logistic regression model, the performance and characteristics of three numerical optimization algorithms–random gradient descent, Mini-Batch random gradient and L-BFGS are studied.

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来源期刊

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

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