Generalized Convolution Spectral Mixture for Multitask Gaussian Processes

Multitask Gaussian processes (MTGPs) are a powerful approach for modeling dependencies between multiple related tasks or functions for joint regression. Current kernels for MTGPs cannot fully model nonlinear task correlations and other types of dependencies. In this article, we address this limitati...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2020-12, Vol.31 (12), p.5613-5623
Hauptverfasser:	Chen, Kai, van Laarhoven, Twan, Groot, Perry, Chen, Jinsong, Marchiori, Elena
Format:	Artikel
Sprache:	eng
Schlagworte:	Convolution Correlation Cross convolution Dependence Frequency-domain analysis Gaussian process Gaussian processes Gaussian processes (GPs) Kernel Kernels multitask learning spectral mixture (SM) Task analysis task dependencies
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