Covering Numbers for Support Vector Machines
Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane in a feature space defined by a kernel function. Until recently, the only bounds on the generalization performance of SV machines (within Valiant's probably approximated correct framework) took no account of the kernel used except in its effect on the margin and radius. More recently, it has been shown that one can bound the relevant covering numbers using tools from functional analysis. In this paper, we...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Information Theory|
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