[1]刘威,刘尚,白润才,等.动态数据约简的神经网络分类器训练方法研究[J].智能系统学报,2017,12(02):2258-2265.[doi:10.11992/tis.201605031]
 LIU Wei,LIU Shang,BAI Runcai,et al.Reducing training times in neural network classifiers by using dynamic data reduction[J].CAAI Transactions on Intelligent Systems,2017,12(02):2258-2265.[doi:10.11992/tis.201605031]
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动态数据约简的神经网络分类器训练方法研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年02期
页码:
2258-2265
栏目:
出版日期:
2017-04-25

文章信息/Info

Title:
Reducing training times in neural network classifiers by using dynamic data reduction
作者:
刘威1 刘尚1 白润才2 周璇1 周定宁1
1. 辽宁工程技术大学 理学院, 辽宁 阜新 123000;
2. 辽宁工程技术大学 矿业学院, 辽宁 阜新 123000
Author(s):
LIU Wei1 LIU Shang1 BAI Runcai2 ZHOU Xuan1 ZHOU Dingning1
1. College of Science, Liaoning Technical University, Fuxin 123000, China;
2. Mining Institute, Liaoning Technical University, Fuxin 123000, China
关键词:
神经网络数据约简分类边界样本权重边界样本核样本
Keywords:
neural networkdata reductionclassification boundarysample weightboundary samplekernel sample
分类号:
TP301.6
DOI:
10.11992/tis.201605031
摘要:
针对神经网络分类器训练时间长、泛化能力差的问题,提出了一种基于动态数据约简的神经网络分类器训练方法(DDR)。该训练方法在训练过程中赋给每个训练样本一个权重值作为样本的重要性度量,依据每次网络迭代训练样本的分类错误率动态更新每个训练样本的权重值,之后依据样本的权重值来约简训练样本,从而增加易错分类的边界样本比重,减少冗余核样本的作用。数值实验表明,基于权重的动态数据约简神经网络训练方法不仅大幅缩短了网络的训练时间,而且还能够显著提升网络的分类泛化能力。
Abstract:
In this paper, we present a neural network classifier training method based on dynamic data reduction (DDR) to address long training times and the poor generalization ability of neural network classifiers. In our approach, we assigned each sample a weight value, which was then dynamically updated based on the classification error rate at each iteration of the training sample. Subsequently, the training sample was reduced based on the weight of the sample so as to increase the proportion of boundary samples in error-prone classification environments and to reduce the role of redundant kernel samples. Our numerical experiments show that our neural network training method not only substantially shortens the training time of the given networks, but also significantly enhances the classification and generalization abilities of the network.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-5-28;改回日期:。
基金项目:国家自然科学基金项目(51304114,71371091).
作者简介:刘威,男,1977年生,副教授,博士,中国计算机学会会员,主要研究方向为人工智能与模式识别、机器学习、露天采矿系统工程;刘尚,男,1988年生,硕士研究生,主要研究方向为人工智能与模式识别、机器学习、计算机视觉;白润才,男,1962年生,教授,博士生导师,主要研究方向为数字矿山、露天开采系统工程。
通讯作者:刘尚. E-mail:whiteinblue@126.com.
更新日期/Last Update: 1900-01-01