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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... (Update)
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BibTeX entry: (Update)
N. Cristianini, A. Elisseeff, J. Shawe-Taylor, and J. Kandola. On kernel target alignment. In Proceedings Neural Information Processing Systems 2001. http://citeseer.ist.psu.edu/cristianini01kerneltarget.html More
@misc{ cristianini-kerneltarget,
author = "Nello Cristianini and John Shawe-Taylor and Andr\'e Elisseeff",
title = "On Kernel-Target Alignment",
url = "citeseer.ist.psu.edu/cristianini01kerneltarget.html" }
Citations (may not include all citations):296 A Probabilistic Theory of Pattern Recognition (context) - Devroye, Gy et al. - 1996
205 An Introduction to Support Vector Machines (context) - Cristianini, Shawe-Taylor - 2000
24 the method of bounded di erences (context) - McDiarmid
4 Latent semantic kernels for feature selection - Cristianini, Lodhi et al. - 2000
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