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Fast on-line learning for multilingual categorization
- Source :
- SIGIR
- Publication Year :
- 2012
- Publisher :
- ACM, 2012.
-
Abstract
- Multiview learning has been shown to be a natural and efficient framework for supervised or semi-supervised learning of multilingual document categorizers. The state-of-the-art co-regularization approach relies on alternate minimizations of a combination of language-specific categorization errors and a disagreement between the outputs of the monolingual text categorizers. This is typically solved by repeatedly training categorizers on each language with the appropriate regularizer. We extend and improve this approach by introducing an on-line learning scheme, where language-specific updates are interleaved in order to iteratively optimize the global cost in one pass. Our experimental results show that this produces similar performance as the batch approach, at a fraction of the computational cost.<br />SIGIR 2012 The 35th International ACM SIGIR conference on research and development in Information Retrieval, August 12-16, 2012, Portland, Oregon, USA
- Subjects :
- Scheme (programming language)
Computer science
business.industry
computer.software_genre
Machine learning
ComputingMethodologies_PATTERNRECOGNITION
Categorization
Line (geometry)
on-line learning
Fraction (mathematics)
Artificial intelligence
multilingual text categorisation
business
computer
Natural language processing
computer.programming_language
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
- Accession number :
- edsair.doi.dedup.....0cb7f32d6658665b1163f1972d9a7383