CSFL: A novel unsupervised convolution neural network approach for visual pattern classification-Reference-Cited by-同舟云学术

CSFL: A novel unsupervised convolution neural network approach for visual pattern classification

Published:2017-08-31 Issue:5 Volume:30 Page:311-324
ISSN:1875-8452
Container-title:AI Communications
language:
Short-container-title:AIC

Author:

ur Rehman Sadaqat¹,Tu Shanshan²,Huang Yongfeng¹,Liu Guojie²

Affiliation:

1. Department of Electronic Engineering, Tsinghua University, 100084 Beijing, China. E-mails: z-sun15@mails.tsinghua.edu.cn, yfhuang@tsinghua.edu.cn

2. Faculty of Information Technology, Beijing University of Technology, 100124 Beijing, China. E-mails: sstu@bjut.edu.cn, jienaever@sina.com

Publisher

IOS Press

Subject

Artificial Intelligence

Reference70 articles.

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3. M. Baccouche, F. Mamalet, C. Wolf, C. Garcia and A. Baskurt, Sparse shift-invariant representation of local 2D patterns and sequence learning for human action recognition, in: 2012 21st International Conference on Pattern Recognition (ICPR), IEEE, 2012, pp. 3823–3826.

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5. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples;Belkin;Journal of Machine Learning Research,2006

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