在线Logistic回归

Online logistic regression

针对连续数据流分类问题,基于在线学习理论,提出一种在线logistic回归算法.研究带有正则项的在线logistic回归,提出了在线logistic-l2回归模型,并给出了理论界估计.最终实验结果表明,随着在线迭代次数的增加,提出的模型与算法能够达到离线预测的分类结果.本文工作为处理海量流数据分类问题提供了一种新的有效方法.

Aiming at the problem of continuous data stream classification,based on online learning theory,an online logistic regression algorithm is proposed.Researching online logistic regression with regular terms,an online logistic-l2 regression model is proposed,and theoretical theoretical estimates are given.The final experimental results show that with the increase of the number of online iterations,the proposed model and algorithm can reach the classification results of offline prediction.The work in this paper provides a new and effective method for dealing with the classification of massive stream data.