de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Time Series Prediction Based on the Relevance Vector Machine with Adaptive Kernels

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83845

Quiñonero-Candela,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Quiñonero-Candela, J. (2002). Time Series Prediction Based on the Relevance Vector Machine with Adaptive Kernels.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E0FD-2
Abstract
The Relevance Vector Machine (RVM) introduced by Tipping is a probabilistic model similar to the widespread Support Vector Machines (SVM), but where the training takes place in a Bayesian framework, and where predictive distributions of the outputs instead of point estimates are obtained. In this paper we focus on the use of RVM's for regression. We modify this method for training generalized linear models by adapting automatically the width of the basis functions to the optimal for the data at hand. Our Adaptive RVM is tried for prediction on the chaotic Mackey-Glass time series. Much superior performance than with the standard RVM and than with other methods like neural networks and local linear models is obtained.