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Τίτλος: Nonparametric Mixtures of Gaussian Processes With Power-Law Behavior
Συγγραφείς: Demiris, Yiannis 
Chatzis, Sotirios P. 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Gaussian Processes;Machine learning approaches;Mixture model;Non-stationary covariance;Nonparametric Bayesian models;Nonparametric mixtures;Variational Bayesian frameworks;Algorithms;Bayesian networks;Gaussian distribution;Gaussian noise (electronic);Mixtures;Space power generation;Process regression;Neural-networks;Models
Ημερομηνία Έκδοσης: Δεκ-2012
Πηγή: IEEE Transactions on Neural Networks and Learning Systems, 2012, vol. 23, no. 12, pp. 1862-1871
Volume: 23
Issue: 12
Start page: 1862
End page: 1871
Περιοδικό: IEEE transactions on neural networks and learning systems 
Περίληψη: Gaussian processes (GPs) constitute one of the most important Bayesian machine learning approaches, based on a particularly effective method for placing a prior distribution over the space of regression functions. Several researchers have considered postulating mixtures of GPs as a means of dealing with nonstationary covariance functions, discontinuities, multimodality, and overlapping output signals. In existing works, mixtures of GPs are based on the introduction of a gating function defined over the space of model input variables. This way, each postulated mixture component GP is effectively restricted in a limited subset of the input space. In this paper, we follow a different approach. We consider a fully generative nonparametric Bayesian model with power-law behavior, generating GPs over the whole input space of the learned task. We provide an efficient algorithm for model inference, based on the variational Bayesian framework, and prove its efficacy using benchmark and real-world datasets. 2012 IEEE.
URI: https://hdl.handle.net/20.500.14279/4128
ISSN: 21622388
DOI: 10.1109/TNNLS.2012.2217986
Rights: © 2012 IEEE
Type: Article
Affiliation: Cyprus University of Technology 
Imperial College London 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

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