On the Extensions of Kernel Alignment
In this paper we address the problem of measuring the degree of agreement between a kernel and a learning task. The quantity that we use to capture this notion is alignment \cite{cris2001}. We motivate its theoretical properties, and derive a series of algorithms for adapting a kernel in two important machine learning problems: regression and classification with uneven datasets. We also propose a novel inductive algorithm within the framework of kernel alignment that can be used for kernel combination and kernel selection. The algorithms presented have been tested on both artificial and real-world datasets.