DocumentCode :
245656
Title :
Web Classification Using Deep Belief Networks
Author :
Shu Sun ; Fang Liu ; Jun Liu ; Yinan Dou ; Hua Yu
Author_Institution :
Beijing Key Lab. of Network, Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2014
fDate :
19-21 Dec. 2014
Firstpage :
768
Lastpage :
773
Abstract :
In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. These deep learning approaches have been applied to image recognition, voice recognition and text processing. However, to our knowledge, the deep learning approaches have not been extensively studied for web data. In this paper, we apply deep belief networks to web data and evaluate the algorithm on various classification experiments by comparing its performance with that of the SVM classification algorithm. In addition, the experiments show good performance of the deep belief networks for various classification tasks.
Keywords :
Internet; belief networks; learning (artificial intelligence); pattern classification; support vector machines; SVM classification algorithm; Web classification; Web data; deep belief networks; deep learning approach; hierarchical representations; unlabeled data; Accuracy; Algorithm design and analysis; Electronic commerce; Support vector machines; Training; Training data; Vectors; DBN; Deep Learning; SVM; Web Classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-7980-6
Type :
conf
DOI :
10.1109/CSE.2014.158
Filename :
7023668
Link To Document :
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