KernelRidgeRegressionDoc < Kernel

---+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.

-- Main.AfshinRostamizadeh - 11 Sep 2009

This topic: Kernel > LearningKernels > KernelRidgeRegressionDoc
Topic revision: r2 - 2009-09-24 - AfshinRostamizadeh