Automatic Kernel Selection Documentation

Command Line Binaries

TODO: specify 3 different input data rather than 3 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.


All of the above command line binaries are used in the following fashion:
$ command  [flags] input_file output_file

The input_file is either an explicit feature mapping representation of the data or a kernel matrix representation, both of which should follow the 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.

TODO: make terminology match techinical def, i.e. weights -> mu

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

  • --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).

-- AfshinRostamizadeh - 10 Sep 2009

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Topic revision: r4 - 2009-09-16 - AfshinRostamizadeh
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