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). -- Main.AfshinRostamizadeh - 10 Sep 2009
This topic: Kernel
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LearningKernels
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LearningKernelsFeatures
Topic revision: r8 - 2011-10-19 - AfshinRostamizadeh
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