Sparse Gaussian Processes on Discrete Domains

Sparse Gaussian Processes on Discrete Domains

Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty e...

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Bibliographic Details
Main Authors: Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Ratsch
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Gaussian processes
machine learning
uncertainty quantification
discrete optimization
Online Access:https://ieeexplore.ieee.org/document/9438670/

Internet

https://ieeexplore.ieee.org/document/9438670/

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