Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

9

JMLR
2010

favoriteEmaildiscussreport

118views more JMLR 2010»

Gaussian processes with monotonicity information

12 years 11 months ago

Gaussian processes with monotonicity information

Download jmlr.csail.mit.edu

A method for using monotonicity information in multivariate Gaussian process regression and classification is proposed. Monotonicity information is introduced with virtual derivative observations, and the resulting posterior is approximated with expectation propagation. Behaviour of the method is illustrated with artificial regression examples, and the method is used in a real world health care classification problem to include monotonicity information with respect to one of the covariates.

Jaakko Riihimäki, Aki Vehtari

Real-time Traffic

Artificial Regression Examples | Gaussian Process Regression | JMLR 2010 | Monotonicity Information |

claim paper

Related Content

» Monotonicity Results for Coherent MIMO Rician Channels

» Bayesian Gaussian Process Classification with the EMEP Algorithm

» Learning Gaussian Process Models from Uncertain Data

» Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Process...

» Information Consistency of Nonparametric Gaussian Process Methods

» Nonlinear Predictive Control with a Gaussian Process Model

» Source coding with intermittent and degraded side information at the decoder

» LQG control over lossy TCPlike networks with probabilistic packet acknowledgements

» Gaussian Process Learning for CyberAttack Early Warning

Post Info
More Details (n/a)

Added	19 May 2011
Updated	19 May 2011
Type	Journal
Year	2010
Where	JMLR
Authors	Jaakko Riihimäki, Aki Vehtari

Comments (0)