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	quadl2	align	alignf
klweightfeatures	any	any		krr	krr
klcombinefeatures	any		svm
klcombinekernels	any		svm	krr	krr	any	any

Note, any data can be formatted to work with klcombinekernels, the programs klcombinefeatures and klweightfeatures however give more efficient implementations of algorithms and allow for more efficient representations of data when possible.

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 (Cortes et al. NIPS 2009).
• align - A positive linear combination of kernels, with the weight of each kernel chosen proportional to its centered kernel target alignment (Cortes et al. ICML 2010).
• alignf - A positive linear combination of kernel, with the weight vector chosen in order to maximize the kernel target alignment of the final combined kernel (Cortes et al. ICML 2010).

-- AfshinRostamizadeh - 10 Sep 2009