Learning Kernels Documentation
Command Line Binaries
There are three main command line binaries that can be used for generating a kernel or feature mapping, depending on how the base matrices are defined. They are listed here in order from the most to least general.
klcombinekernels - given several kernel matrices for the same dataset, output a weighted combination of these kernel matrices.
klcombinefeatures - given several explicit feature mappings of the same dataset, output a weighted combination of these feature mappings.
klweightfeatures - given a single explicit feature mapping of the dataset, output a weighted feature mapping or kernel. I.e. each feature in this case corresponds to a rank-1 kernel.
All of the above command line binaries are used in the following fashion:
$ command [flags] input_file output_file
is either an explicit feature mapping representation of the data or a kernel matrix representation, both of which should follow the LIBSVM format
can read binary kernel files output by the
tool with the
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.
--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.
--lk_alg - Select which kernel combination method is used. See feature table to see which combination methods are available with each command line binary.
--max_iter - The maximum number of iterations used by any iterative kernel combination algorithm.
--mu_file - Save the kernel combination weights to the specified file.
--num_train - Give argument
p:q to select points
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.
Command Specific Flags
--read_kar - expect the input to be a binary kernel matrix.
--label_file - this flag must be set when the
--read_kar flag is used. Should point to the location of a text file with one label per line, which corresponds to the binary matrix input.
--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).
- 10 Sep 2009