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Technical Report NC-TR-02-120
2002-120
On the Extensions of Kernel Alignment
Jaz Kandola
John Shawe-Taylor
Nello Cristianini
ABSTRACT
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.
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