Deep Belief Networks and deep learning

Yuming Hua, Junhai Guo, Hua Zhao
{"title":"Deep Belief Networks and deep learning","authors":"Yuming Hua, Junhai Guo, Hua Zhao","doi":"10.1109/ICAIOT.2015.7111524","DOIUrl":null,"url":null,"abstract":"Deep Belief Network is an algorithm among deep learning. It is an effective method of solving the problems from neural network with deep layers, such as low velocity and the overfitting phenomenon in learning. In this paper, we will introduce how to process a Deep Belief Network by using Restricted Boltzmann Machines. What is more, we will combine the Deep Belief Network together with softmax classifier, and use it in the recognition of handwritten numbers.","PeriodicalId":310429,"journal":{"name":"Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"108","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIOT.2015.7111524","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 108

Abstract

Deep Belief Network is an algorithm among deep learning. It is an effective method of solving the problems from neural network with deep layers, such as low velocity and the overfitting phenomenon in learning. In this paper, we will introduce how to process a Deep Belief Network by using Restricted Boltzmann Machines. What is more, we will combine the Deep Belief Network together with softmax classifier, and use it in the recognition of handwritten numbers.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
深度信念网络和深度学习
深度信念网络是深度学习中的一种算法。它是解决深层神经网络学习中速度慢、过拟合现象等问题的有效方法。本文将介绍如何使用受限玻尔兹曼机来处理深度信念网络。此外,我们将Deep Belief Network与softmax分类器相结合,并将其用于手写数字的识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
One methodology for spam review detection based on review coherence metrics Visual tracking via weighted sparse representation A condition monitoring algorithm based on image geometric analysis for substation switch A rank sequence method for detecting black hole attack in ad hoc network Distributed CoMP transmission for cell range expansion with almost blank subframe in downlink heterogeneous networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1