Bagging for Gaussian process regression

Authors:
Tao Chen;Jianghong Ren
Affiliations:
School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore;College of Automation, Chongqing University, Chongqing 400044, China
Venue:
Neurocomputing
Year:
2009

Citing 11
Cited 9

Original Contribution: Stacked generalization

Neural Networks
Bagging predictors

Machine Learning
Inferential estimation of polymer quality using bootstrap aggregated neural networks

Neural Networks
Bayesian Learning for Neural Networks

Bayesian Learning for Neural Networks
Improving nonparametric regression methods by bagging and boosting

Computational Statistics & Data Analysis - Nonlinear methods and data mining
Sparse on-line Gaussian processes

Neural Computation
Gaussian Processes for Ordinal Regression

The Journal of Machine Learning Research
A Bayesian Committee Machine

Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Bagging Linear Sparse Bayesian Learning Models for Variable Selection in Cancer Diagnosis

IEEE Transactions on Information Technology in Biomedicine
Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging

IEEE Transactions on Neural Networks

Exploration of bagging ensembles comprising genetic fuzzy models to assist with real estate appraisals

IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers

Neurocomputing
Marginalized neural network mixtures for large-scale regression

IEEE Transactions on Neural Networks
Analysis of bagging ensembles of fuzzy models for premises valuation

ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets

The Journal of Machine Learning Research
Greener aviation with virtual sensors: a case study

Data Mining and Knowledge Discovery
GPLP: a local and parallel computation toolbox for Gaussian process regression

The Journal of Machine Learning Research
Large-scale Gaussian process classification using random decision forests

Pattern Recognition and Image Analysis
Neural network ensemble operators for time series forecasting

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper proposes the application of bagging to obtain more robust and accurate predictions using Gaussian process regression models. The training data are re-sampled using the bootstrap method to form several training sets, from which multiple Gaussian process models are developed and combined through weighting to provide predictions. A number of weighting methods for model combination are discussed, including the simple averaging and the weighted averaging rules. We propose to weight the models by the inverse of their predictive variance, and thus the prediction uncertainty of the models is automatically accounted for. The bagging method for Gaussian process regression is successfully applied to the inferential estimation of quality variables in an industrial chemical plant.