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.
krrtrain
Usage: krrtrain [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 datastructure.

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 lowrank approximation of the kernel matrix (between 1 and # of training points).
Prediction
Usage: krrpredict [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 datastructure.
 AfshinRostamizadeh  11 Sep 2009 