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NeuroCOLT
Technical Report NC-TR-01-099
2001-099
On Kernel Target
Alignment
Nello Cristianini
Andre Elisseeff
John Shawe-Taylor
Jaz Kandla
ABSTRACT
We introduce the notion of kernel-alignment, a measure of similarity between two kernel functions or between a kernel
and a target function. This quantity captures the degree of agreement between a kernel and a given learning task, and has very natural
interpretations in machine learning, leading also to simple algorithms for model selection and learning. We analyse its theoretical properties,
proving that it is sharply concentrated around its expected value, and we discuss its relation with other standard measures of
performance. Finally we describe some of the algorithms that can be obtained within this framework, giving experimental results
showing that adapting the kernel to improve alignment on the labelled data significantly increases the alignment on the test set,
giving improved classification accuracy. Hence, the approach provides a principled method of performing transduction.
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