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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2022
    • vol. 7, nº 5, september 2022
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2022
    • vol. 7, nº 5, september 2022
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    Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization

    Autor: 
    Ling, Yongfa
    ;
    Guan, Wenbo
    ;
    Ruan, Qiang
    ;
    Song, Heping
    ;
    Lai, Yuping
    Fecha: 
    09/2022
    Palabra clave: 
    bayesian inference; extended variational inference; mixture model; text categorization; inverted beta-liouville distribution; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/13710
    DOI: 
    https://doi.org/10.9781/ijimai.2022.08.006
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/3157
    Open Access
    Resumen:
    he finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization of the variational posterior distribution cannot be obtained, since the variational object function involves evaluation of intractable moments. With the recently proposed extended variational inference (EVI) framework, a new function is proposed to replace the original variational object function in order to avoid intractable moment computation, so that the analytically tractable solution of the IBLMM can be derived in an effective way. The good performance of the proposed approach is demonstrated by experiments with both synthesized data and a real-world application namely text categorization.
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