Learning Mixtures of Truncated Basis Functions from Data

Learning Mixtures of Truncated Basis Functions from Data

In this paper we investigate methods for learning hybrid Bayesian networks from data. First we utilize a kernel density estimate of the data in order to translate the data into a mixture of truncated basis functions (MoTBF) representation using a convex optimization technique. When utilizing a kerne...

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Bibliographic Details
Main Authors: Langseth, Helge, Nielsen, Thomas D., Pérez-Bernabé, Inmaculada, Salmerón Cerdán, Antonio
Format: info:eu-repo/semantics/article
Language:English
Published: 2017
Subjects:
Mixtures of truncated basis functions
Hybrid Bayesian networks
Learning
Online Access:http://hdl.handle.net/10835/4894
https://doi.org/10.1016/j.ijar.2013.09.012

Internet

http://hdl.handle.net/10835/4894
https://doi.org/10.1016/j.ijar.2013.09.012

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