查询结果:   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.
中文标题
基于并行Adaboost-BP网络的大规模在线学习行为评价
发表栏目
算法
摘要点击数
683
英文标题
EVALUATION OF LARGE-SCALE ONLINE LEARNING BEHAVIOR BASED ON PARALLEL ADABOOST-BP NETWORK
作 者
曹建芳 郝耀军 Cao Jianfang Hao Yaojun
作者单位
忻州师范学院计算机科学与技术系 山西 忻州 034000     
英文单位
Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou 034000, Shanxi,China     
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
Keywords
Adaboost-BP neural network Online learning behavior Feature extraction MapReduce parallel programming model
基金项目
山西省自然科学基金项目(2013011017-2);山西省高等学校教学改革重点项目(J2015099);2014年度忻州师范学院重点学科专项课题(XK201308)
作者资料
曹建芳,教授,主研领域:智能信息处理,大数据技术。郝耀军,副教授。 。
文章摘要
针对传统的在线学习行为评价方法在处理大规模数据集时面临的问题,提出一种基于并行Adaboost-BP神经网络的在线学习行为评价方法。将BP神经网络作为弱预测器,由Adaboost算法组合15个BP神经网络的输出,构建了强预测器;充分利用了Hadoop平台下MapReduce并行编程模型,提出了大规模在线学习行为的自动评价模型,设计了并行Adaboost-BP神经网络算法的Map和Reduce任务。多组实验表明,提出的算法准确率高、运行耗时少,取得了良好的加速比,效率大于0.5,适合大规模在线学习行为的自动评价。
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.
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