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• W982949649 abstract "Hidden Markov models (HMMs) are known for their ability to well model and easily handle variable length time-series. Their use in the case of proportional data modeling has been seldom mentioned in the literature. However, proportional data are a common way of representing large data in a compact fashion and often arise in pattern recognition applications frameworks. HMMs have been first developed for discrete and Gaussian data and their extension to proportional data through the use of Dirichlet distributions is quite recent. The Dirichlet distribution has its limitations and is a special case of the more general generalized Dirichlet (GD) distribution that suffers from less restrictions on the modeled data. We propose here to derive the equations and the methodology of a GD-based HMM and to assess its superiority over a Dirichlet-based HMM (HMMD) through experiments conducted on both synthetic and real data." @default.
• W982949649 created "2016-06-24" @default.
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• W982949649 date "2014-01-01" @default.
• W982949649 modified "2023-10-11" @default.
• W982949649 title "Hidden Markov Models Based on Generalized Dirichlet Mixtures for Proportional Data Modeling" @default.
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• W982949649 doi "https://doi.org/10.1007/978-3-319-11656-3_7" @default.
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