计算机应用与软件2017，Vol.34Issue(7)：267-272,6.DOI:10.3969/j.issn.1000-386x.2017.07.049
基于并行Adaboost-BP网络的大规模在线学习行为评价
EVALUATION OF LARGE-SCALE ONLINE LEARNING BEHAVIOR BASED ON PARALLEL ADABOOST-BP NETWORK
摘要
Abstract
Aiming at the problems that traditional online learning behavior evaluation methods face when dealing with large-scale data sets, an online learning behavior evaluation method based on parallel Adaboost-BP neural network is proposed.The BP neural network was used as the weak predictor, and 15 BP neural networks were combined by the Adaboost algorithm to construct the strong predictor.The MapReduce parallel programming model of Hadoop platform was fully utilized.An automatic evaluation model of large-scale online learning behavior was proposed.The Map and Reduce tasks of parallel Adaboost-BP neural network algorithm were designed.The experimental results show that the proposed algorithm has high accuracy rate, low running time and good speedup ratio.The efficiency is more than 0.5, which is suitable for the automatic evaluation of large-scale online learning behavior.关键词
Adaboost-BP神经网络/在线学习行为/特征提取/MapReduce并行编程模型Key words
Adaboost-BP neural network/ Online learning behavior/ Feature extraction/ MapReduce parallel programming model分类
信息技术与安全科学引用本文复制引用
曹建芳,郝耀军..基于并行Adaboost-BP网络的大规模在线学习行为评价[J].计算机应用与软件,2017,34(7):267-272,6.基金项目
山西省自然科学基金项目(2013011017-2) （2013011017-2）
山西省高等学校教学改革重点项目(J2015099) （J2015099）
2014年度忻州师范学院重点学科专项课题(XK201308). （XK201308）
