Exact O(N2) Hyper-Parameter Optimization for Gaussian Process Regression

Yijue Dai
Yijue Dai
Ceyao Zhang
Ceyao Zhang

MLSP, pp. 1-6, 2020.

Cited by: 0|Views5
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Abstract:

Hyper-parameter optimization remains as the core issue of Gaussian process (GP) for machine learning nowadays. The benchmark method using maximum likelihood (ML) estimation and gradient descent (GD) is impractical for processing big data due to its O(n3) complexity. Many sophisticated global or local approximation models have been propose...More

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