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» Heteroscedastic Gaussian process regression
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ILP
2003
Springer
15 years 4 months ago
Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Thomas Gärtner, Kurt Driessens, Jan Ramon
CDC
2008
IEEE
130views Control Systems» more  CDC 2008»
15 years 6 months ago
Predictor estimation via Gaussian regression
Abstract— A novel nonparametric paradigm to model identification has been recently proposed where, in place of postulating finite-dimensional models of the system transfer func...
Gianluigi Pillonetto, Alessandro Chiuso, Giuseppe ...
94
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NIPS
2007
15 years 1 months ago
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
Ruslan Salakhutdinov, Geoffrey E. Hinton
CVPR
2010
IEEE
15 years 2 months ago
Multi-Task Warped Gaussian Process for Personalized Age Estimation
Automatic age estimation from facial images has aroused research interests in recent years due to its promising potential for some computer vision applications. Among the methods ...
Yu Zhang, Dit-Yan Yeung
ICIP
2006
IEEE
16 years 1 months ago
Estimating Illumination Chromaticity via Kernel Regression
We propose a simple nonparametric linear regression tool, known as kernel regression (KR), to estimate the illumination chromaticity. We design a Gaussian kernel whose bandwidth i...
Vivek Agarwal, Andrei V. Gribok, Andreas Koschan, ...