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Old 07-23-2013, 07:05 PM
hsolo hsolo is offline
Join Date: Jul 2013
Posts: 12
Default Learning performance comparable to SVM that doesnt require QP

1. Is there any learning algorithm/approach that offers, in practice, performance comparable to SVM but that doesnt require QP?

For eg,

2. At a high level is it correct to think of regularization as introducing a 'softness' and thus a 'generalization dividend' for regression problems and of SVMs (soft kernels) as introducing a generalization dividend for classification problems, albeit at the cost of QP?
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