(Enter summary)
Abstract: We present an extension to the Mixture of Experts (ME) model, where
the individual experts are Gaussian Process (GP) regression models. Using
an input-dependent adaptation of the Dirichlet Process, we implement
a gating network for an infinite number of Experts. Inference in this
model may be done efficiently using a Markov Chain relying on Gibbs
sampling. The model allows the effective covariance function to vary
with the inputs, and may handle large datasets -- thus potentially... (Update)
Cited by: More
Hierarchical Topic Models and - The Nested Chinese
(Correct)
Nonparametric empirical Bayes for the Dirichlet process.. - Jon Mcauliffe David
(Correct)
Dirichlet Enhanced Latent Semantic Analysis - Kai Yu Kai
(Correct)
Similar documents (at the sentence level):
71.4%: Infinite Mixtures of Gaussian Process Experts - Rasmussen, Ghahramani (2002)
(Correct)
Similar documents based on text: More All
0.8: Bayesian Monte Carlo - Rasmussen, Ghahramani
(Correct)
0.7: Occam's Razor - Rasmussen, Ghahramani (2001)
(Correct)
0.5: The Infinite Hidden Markov Model - Beal, Ghahramani, Rasmussen (2002)
(Correct)
Related documents from co-citation: More All
5: A Bayesian analysis of some nonparametric problems (context) - Ferguson - 1973
5: Markov chain sampling methods for Dirichlet process mixture models (context) - Neal - 1998
4: Bayesian learning for neural networks (context) - Neal - 1995
BibTeX entry: (Update)
Carl E. Rasmussen and Zoubin Ghahramani, "Infinite mixtures of gaussian process experts," in Advances in Neural Information Processing Systems, 2002, number 14. http://citeseer.ist.psu.edu/article/rasmussen01infinite.html More
@misc{ rasmussen02infinite,
author = "C. Rasmussen and Z. Ghahramani",
title = "Infinite mixtures of gaussian process experts",
text = "Carl E. Rasmussen and Zoubin Ghahramani, Infinite mixtures of gaussian
process experts, in Advances in Neural Information Processing Systems, 2002,
number 14.",
year = "2002",
url = "citeseer.ist.psu.edu/article/rasmussen01infinite.html" }
Citations not processed or no citations identified.
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www-2.cs.cmu.edu/Groups/NIPS/NIPS2001/papers/): More
Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks - Schmitt (2001)
(Correct)
Effective Size of Receptive Fields of Inferior.. - Trappenberg, Rolls.. (2001)
(Correct)
Matching Free Trees with Replicator Equations - Pelillo (2001)
(Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC