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{krr,svm,any} indicates whether the algorithm is designed to optimize the kernel ridge regression (KRR) or support vector machine (SVM) objective.  
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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.  
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{krr,svm,any} indicates whether the algorithm is designed to optimize the kernel ridge regression (KRR) or support vector machine (SVM) objective.  
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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.
 
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 AfshinRostamizadeh  10 Sep 2009  
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{krr,svm,any} indicates whether the algorithm is designed to optimize the kernel ridge regression (KRR) or support vector machine (SVM) objective.  
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> >  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:

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{krr,svm,any} indicates whether the algorithm is designed to optimize the kernel ridge regression (KRR) or support vector machine (SVM) objective.  
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The kernel learning algorithms are summarized as follows:
 
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 AfshinRostamizadeh  10 Sep 2009 
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{krr,svm,any} indicates whether the algorithm is designed to optimize the kernel ridge regression (KRR) or support vector machine (SVM) objective.  
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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.  
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 AfshinRostamizadeh  10 Sep 2009 
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> >  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.  
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> >  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.  
 AfshinRostamizadeh  10 Sep 2009 \ No newline at end of file 
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 AfshinRostamizadeh  10 Sep 2009 