JGPR: a computationally efficient multi-target Gaussian process regression algorithm

DOI10.1007/s10994-022-06170-3OpenAlexW4280634883MaRDI QIDQ2163240

Seyed Ali Ghorashi, Reza Shahbazian, Mohammad Nabati

Publication date: 10 August 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-022-06170-3

zbMATH Keywords

machine learningGaussian process regressionmulti-task learningmulti-target regression

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

JGPR

Uses Software

• UCI-ml
• FORS
• ElemStatLearn
• George

Cites Work

• First order regression
• Multi-target support vector regression via correlation regressor chains
• Feature ranking for multi-target regression
• Convex multi-task feature learning
• Multi-target regression via input space expansion: treating targets as inputs
• Stochastic variational hierarchical mixture of sparse Gaussian processes for regression
• SVM Multiregression for Nonlinear Channel Estimation in Multiple-Input Multiple-Output Systems
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