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---+ Automatic Kernel Selection Documentation ---++ Command Line Binaries There are three main command line binaries that can be used for generating a kernel or feature mapping: * =klweightfeatures= - given an explicit feature mapping of the dataset, output a weighted feature mapping or kernel. * =klcombinefeatures= - given several explicit feature mappings of the same dataset, output a weighted combination of these feature mappings. * =klcombinekernels= - given several kernel matrices for the same dataset, output a weighted combination of these kernel matrices. ---+++ Usage All of the above command line binaries are used in the following fashion: <verbatim> $ command [flags] input_file output_file </verbatim> The =input_file= is either an explicit feature mapping representation of the data or a kernel matrix representation, both of which should follow the [[http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/][LIBSVM format]]. The =output_file= is also going to be an explicit feature mapping representation or kernel matrix representation of the data in LIBSVM format. ---++ Command Line Flags Here we give a list of useful flags that are used in conjunction with the command line binaries. Note, running any of the commands without any arguments will result in a full listing and description of possible flags and their default values. ---+++ General Flags * =--alg_reg= - For algorithm specific kernel combination methods, use the specified algorithm regularization parameter. * =--ker_reg= - Specify kernel regularization parameter use within the kernel combination algorithm. * =--interp_param= - The interpolation parameter used with iterative kernel combination algorithms, chosen between 0 and 1. A value closer to 0 will lead to larger steps, but also possible instability. * =--max_iter= - The maximum number of iterations used by any iterative kernel combination algorithm. * =--num_train= - Give argument =p:q= to select points =p= through =q= for training the matrix. The learned kernel function is still applied to the entire dataset. * =--sparse= - Used sparse data-structures when given feature mappings as input. * =--tol= - Specify tolerance of stopping criteria for iterative kernel combination algorithms. Smaller values lead to more precise answers, but longer convergence times. * =--weight_file= - Save the combination weights to the specified file. * =--weight_type= - Select which kernel combination method is used. See feature table to see which combination methods are available with each command line binary. ---+++ Command Specific Flags *klweightfeatures:* * =--features= - print output using explicit feature mappings instead of computing the kernel. * =--offset= - include the specified constant value as an additional feature (can be used to act as an offset). -- Main.AfshinRostamizadeh - 10 Sep 2009
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Topic revision: r3 - 2009-09-12
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AfshinRostamizadeh
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