燕山大学学报2015，Vol.39Issue(4)：283-291,9.DOI:10.39+9/j.issn.1007-791X.2015.04.001
卷积神经网络分类模型在模式识别中的新进展
Rrcrnt progrrss on convolutional nrural nrtwork in pattrrn rrcognition
摘要
Abstract
Recently,deep learning has attracted more and more attention as a new research area in machine learning.The deep net-work model constructed from deep learning shows the excellent performance in unsupervised feature extraction.Convolutional neural network ( CNN) is a relative successful deep learning model and it has gradually become the focus of current research. A general progress overview for CNN and its new progress in pattern recognition are gave.The basic situation of deep learning and CNN are in-troduced at first,including the fundamental principles of CNN,the relationship about deep learning and CNN.Then,the improved al-gorithms about CNN and its new applications in pattern recognition are summarized.Finally,the issues about CNN need to solve in the future are discussed.关键词
深度学习/卷积神经网络/无监督特征提取/模式识别Key words
deep learning/convolutional neural network/unsupervised feature extraction/pattern recognition分类
信息技术与安全科学引用本文复制引用
胡正平,陈俊岭,王蒙,赵淑欢..卷积神经网络分类模型在模式识别中的新进展[J].燕山大学学报,2015,39(4):283-291,9.基金项目
国家自然科学基金资助项目(61071199) （61071199）
河北省自然科学基金资助项目(F2010001297) （F2010001297）
