Self-learning recursive neural networks for structured data classification

This data was imported from Scopus:

Authors: Bouchachia, A. and Ortner, A.

Journal: Proceedings of the International Joint Conference on Neural Networks

Pages: 808-815

ISBN: 9781479914845

DOI: 10.1109/IJCNN.2014.6889804

© 2014 IEEE. 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.

This data was imported from Web of Science (Lite):

Authors: Bouchachia, A., Ortner, A. and IEEE


Pages: 808-815

ISSN: 2161-4393

The data on this page was last updated at 04:57 on June 24, 2019.