On-line Prediction with Kernels and the Complexity Approximation Principle
Alex Gammerman, Yuri Kalnishkan, Vladimir Vovk
The paper describes an application of Aggregating Algorithm to the problem of regression. It generalizes earlier results concerned with plain linear regression to kernel techniques and presents an on-line algorithm which performs nearly as well as any oblivious kernel predictor. The paper contains the derivation of an estimate on the performance of this algorithm. The estimate is then used to derive an application of the Complexity Approximation Principle to kernel methods.
PDF Link: /papers/04/p170-gammerman.pdf
AUTHOR = "Alex Gammerman
and Yuri Kalnishkan and Vladimir Vovk",
TITLE = "On-line Prediction with Kernels and the Complexity Approximation Principle",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "170--176"