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New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

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New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

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Safont Armero, G. (2015). New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods [Tesis doctoral no publicada]. Universitat Politècnica de València. doi:10.4995/Thesis/10251/53913.

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/53913

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Title: New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods
Author:
Director(s): Salazar Afanador, Addisson Vergara Domínguez, Luís
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Read date / Event date:
2015-07-13
Issued date:
Abstract:
[EN] This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component ...[+]


[ES] Esta tesis considera nuevas aplicaciones de las mezclas no Gaussianas dentro del marco de trabajo del procesado estadístico de señal y del reconocimiento de patrones. Las mezclas no Gaussianas fueron implementadas ...[+]


[CAT] Aquesta tesi considera noves aplicacions de barreges no Gaussianes dins del marc de treball del processament estadístic de senyal i del reconeixement de patrons. Les barreges no Gaussianes van ser implementades ...[+]
Subjects: Independent component analysis , Finite mixture models , Data prediction , Classification , Pattern recognition , Non-destructive testing , Electroencepalography
Copyrigths: Reserva de todos los derechos
DOI: 10.4995/Thesis/10251/53913
Type: Tesis doctoral

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