Self-learning recursive neural networks for structured data classification
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
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.
Source: Scopus
Self-learning Recursive Neural Networks for Structured Data Classification
Authors: Bouchachia, A. and Ortner, A.
Journal: PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Pages: 808-815
ISSN: 2161-4393
Source: Web of Science (Lite)