Kernel Ridge Regression Documentation

The KRR tools are split into two binaries, one for training and one for prediction. The data file consists of either explicit feature vectors or a kernel matrix, both of which should be in LIBSVM format.

krr-train

Usage: krr-train [flags] data_file regularization_parameter [model_output_file]

Flags:

  • kernel - The data_file contains a kernel matrix, as oppose to feature vectors.
  • sparse - Represent the feature vectors using a sparse data-structure.
  • dual - Force the KRR problem to be solved in the dual.
  • primal - Force the KRR problem to be solved in the primal.
  • approx - Specify the rank to be used with a low-rank approximation of the kernel matrix (between 1 and # of training points).

Prediction

Usage: krr-predict [flags] data_file model [predictions]

Flags:

  • kernel - The data_file contains a kernel matrix, as oppose to feature vectors.
  • sparse - Represent the feature vectors using a sparse data-structure.

-- AfshinRostamizadeh - 11 Sep 2009

Topic revision: r2 - 2009-09-24 - AfshinRostamizadeh
 
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