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.

command\KL algorithm unif corr lin1 lin2 quadl2Sorted ascending
klweightfeatures any any   krr krr
klcombinekernels any   svm krr krr
klcombinefeatures any   svm    

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. (Cortes et al. UAI 2009)
  • quadl2 - A positive quadratic combination of kernels, regularization restricts the l2-norm of the weights.

-- AfshinRostamizadeh - 10 Sep 2009

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Topic revision: r4 - 2009-09-16 - AfshinRostamizadeh
 
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