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 [BibTeX] [Marc21]
Boosting HMMs with an application to speech recognition
Type of publication: Idiap-RR
Citation: DimitrakBengio2003a
Number: Idiap-RR-41-2003
Year: 2003
Institution: IDIAP
Note: Accepted for publication in ICASSP 2004
Abstract: Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines its applicability to sequence learning problems. In particular, different methods for training HMMs on sequences and for combining their output are investigated in the context of automatic speech recognition.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Dimitrakakis, Christos
Bengio, Samy
Crossref by icassp:DimitrakBengio2004
Added by: [UNK]
Total mark: 0
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