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- W2034830182 abstract "Automatic classification of structured data is a challenging task and its relevance to many domains is evident. However, collecting labeled data may turn to be a quite expensive task and sometimes even prone to mislabeling. A technical solution to this problem consists in combining few labeled data samples and a significant amount of unlabeled data samples to train a classifier. Likewise, the present paper deals with the classification of partially labeled tree-like structured data. To carry on this task, we suggest an adapted variant of recursive neural networks (RNNs) that is equipped with semi-supervision mechanisms capable of learning from labeled and unlabeled tree-like data. Accordingly RNNs rely on self-learning to actively pre-label data which will be combined with originally labeled one during the learning process. The semi-supervised RNNs approach is presented and evaluated on real-world extensible Markup Language (XML) collection of documents in the context of digital libraries. The initial empirical experiments show high quality results." @default.
- W2034830182 created "2016-06-24" @default.
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- W2034830182 date "2014-07-01" @default.
- W2034830182 modified "2023-09-25" @default.
- W2034830182 title "Self-learning recursive neural networks for structured data classification" @default.
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- W2034830182 doi "https://doi.org/10.1109/ijcnn.2014.6889804" @default.
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