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NeuroCOLT
Technical Report NC-TR-01-087
2001-087
On Optimizing Kernel
Alignment
Nello Cristianini
Jaz Kandola
Andre Elisseeff
John Shawe-Taylor
ABSTRACT
We address the problem of measuring the degree of agreement between
a kernel and a learning task. We propose a quantity to capture this
notion, which we call Alignment. We study its theoretical properties,
and derive a series of algorithms for adapting a kernel to the labels
and vice versa. This produces a series of novel methods for clustering
and transduction, kernel combination and kernel selection.
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