Learning performance comparable to SVM that doesnt require QP

[hsolo]

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

For eg, http://cbcl.mit.edu/cbcl/publications/ps/rlsc.pdf

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?

[yaser]

Originally Posted by hsolo

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

For eg, http://cbcl.mit.edu/cbcl/publications/ps/rlsc.pdf

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?

1. Different techniques outperform others in different problems, so other methods, including the one you mention, will indeed beat SVM in some cases. None of the methods wins in all applications.

2. This is a legitimate way of looking at it.