Here we list which kernel learning (KL) methods are implemented within each command line binary. The entry
{krr,svm,any}
indicates whether the algorithm is designed to optimize the kernel ridge regression (KRR) or support vector machine (SVM) objective.
The kernel learning algorithms are summarized as follows:
-
unif
- A uniform linear combination of base kernels/features, regularization restricts the trace of the kernel matrix.
-
corr
- Weight each feature proportional to its correlation with the labels, regularization restricts the trace of the kernel matrix.
-
lin1
- A positive linear combination of kernels, regularization restricts the kernel matrix trace. (Lanckriet et al., JMLR 2004, Cortes et al., MLSP 2008)
-
lin2
- A positive linear combination of kernels, regularization restricts the l2-norm of the weights.
-
quadl2
- A positive quadratic combination of kernels, regularization restricts the l2-norm of the weights.
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AfshinRostamizadeh - 10 Sep 2009