Variational Gaussian process classifiers

Authors:
M. N. Gibbs;D. J.C. Mackay
Affiliations:
Cavendish Lab., Cambridge Univ., UK;-
Venue:
IEEE Transactions on Neural Networks
Year:
2000

Citing 0
Cited 18

Gaussian process classification for segmenting and annotating sequences

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Variational Bayesian multinomial probit regression with Gaussian process priors

Neural Computation
Appearance-based gender classification with Gaussian processes

Pattern Recognition Letters
Assessing Approximate Inference for Binary Gaussian Process Classification

The Journal of Machine Learning Research
Clustering Based on Gaussian Processes

Neural Computation
Bayes Machines for binary classification

Pattern Recognition Letters
Nonlinear system identification: From multiple-model networks to Gaussian processes

Engineering Applications of Artificial Intelligence
Outlier Robust Gaussian Process Classification

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Gaussian process approach for modelling of nonlinear systems

Engineering Applications of Artificial Intelligence
Validation-based sparse gaussian process classifier design

Neural Computation
Variational GTM

IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Gaussian Processes for Machine Learning (GPML) Toolbox

The Journal of Machine Learning Research
Using Gaussian process based kernel classifiers for credit rating forecasting

Expert Systems with Applications: An International Journal
Divergence measures and a general framework for local variational approximation

Neural Networks
Robust Gaussian Process Regression with a Student-t Likelihood

The Journal of Machine Learning Research
A generic model to compose vision modules for holistic scene understanding

ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Evidential classifier for imprecise data based on belief functions

Knowledge-Based Systems
Gaussian Kullback-Leibler approximate inference

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Gaussian processes are a promising nonlinear regression tool, but it is not straightforward to solve classification problems with them. In the paper the variational methods of Jaakkola and Jordan (2000) are applied to Gaussian processes to produce an efficient Bayesian binary classifier.