Two-hidden-layer extreme learning machine based wrist vein recognition system

  • Published: 01 January 2017
  • Text categorization is the fundamental bricks of other related researches in NLP. Up to now, researchers have proposed many effective text categorization methods and gained well performance. However, these methods are generally based on the raw features or low level features, e.g., tf or tfidf, while neglecting the semantic structures between words. Complex semantic information can influence the precision of text categorization. In this paper, we propose a new method to handle the semantic correlations between different words and text features from the representations and the learning schemes. We represent the document as multiple instances based on word2vec. Experiments validate the effectiveness of proposed method compared with those state-ofthe-art text categorization methods.

    Citation: Jian-Bing Zhang, Yi-Xin Sun, De-Chuan Zhan. Two-hidden-layer extreme learning machine based wrist vein recognition system[J]. Big Data and Information Analytics, 2017, 2(1): 69-76. doi: 10.3934/bdia.2017009

    Related Papers:

  • Text categorization is the fundamental bricks of other related researches in NLP. Up to now, researchers have proposed many effective text categorization methods and gained well performance. However, these methods are generally based on the raw features or low level features, e.g., tf or tfidf, while neglecting the semantic structures between words. Complex semantic information can influence the precision of text categorization. In this paper, we propose a new method to handle the semantic correlations between different words and text features from the representations and the learning schemes. We represent the document as multiple instances based on word2vec. Experiments validate the effectiveness of proposed method compared with those state-ofthe-art text categorization methods.


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