KIP

2000년 도입 · 논문 3편에서 사용

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 $\epsilon$-approximate datasets. It can be regarded as an adaption of the inducing point method for Gaussian processes to the case of Kernel Ridge Regression.

출처: Dataset Meta-Learning from Kernel Ridge-Regression

소개 논문: Dataset Meta-Learning from Kernel Ridge-Regression