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Chinese text categorization based on deep belief networks | IEEE Conference Publication | IEEE Xplore

Chinese text categorization based on deep belief networks


Abstract:

With the rapid development of Internet, text categorization becomes a mission-critical technology that organizes and processes large amounts of data in document. Deep bel...Show More

Abstract:

With the rapid development of Internet, text categorization becomes a mission-critical technology that organizes and processes large amounts of data in document. Deep belief networks have powerful abilities of learning and can extract highly distinguishable features from the high-dimensional original feature space. So a new Chinese text categorization algorithm based on deep learning structure and semi-supervised deep belief networks is presented in this paper. We extract original feature with TFIDF-ICF, construct the text classification model based on DBN, and select the number of hidden layers and hidden units. Our experimental results indicated that the performance of text categorization algorithm based on deep belief networks is better than support vector machine.
Date of Conference: 26-29 June 2016
Date Added to IEEE Xplore: 25 August 2016
ISBN Information:
Conference Location: Okayama, Japan

References

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