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What is: Kernel Inducing Points?

SourceDataset Meta-Learning from Kernel Ridge-Regression
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

Kernel Inducing Points, or KIP, is a meta-learning algorithm for learning datasets that can mitigate the challenges which occur for naturally occurring datasets without a significant sacrifice in performance. KIP uses kernel-ridge regression to learn ϵ\epsilon-approximate datasets. It can be regarded as an adaption of the inducing point method for Gaussian processes to the case of Kernel Ridge Regression.