Journal article
Hybrid hierarchical classifiers for categorization of medical documents
Proceedings of the American Society for Information Science and Technology, Vol.40(1), pp.65-70
10/2003
DOI: 10.1002/meet.1450400108
Abstract
This article presents a study of the application of hierarchical classifiers based on the hierarchical mixtures of experts. In particular we present an extension of our work that explores the use of linear classifiers and a hybrid model that combines backpropagation neural networks with linear classifiers. We test this model using the UMLS as the classification structure and a subset of medical s from MEDLINE. Our results confirm that using the hierarchical structure of the classification vocabulary improves categorization performance.
Details
- Title: Subtitle
- Hybrid hierarchical classifiers for categorization of medical documents
- Creators
- Miguel E RuizPadmini Srinivasan
- Resource Type
- Journal article
- Publication Details
- Proceedings of the American Society for Information Science and Technology, Vol.40(1), pp.65-70
- Publisher
- Wiley Subscription Services, Inc., A Wiley Company; Hoboken
- DOI
- 10.1002/meet.1450400108
- ISSN
- 0044-7870
- eISSN
- 1550-8390
- Number of pages
- 6
- Language
- English
- Date published
- 10/2003
- Academic Unit
- Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984003013102771
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