Package smile.base.svm

Class SVR<T>

java.lang.Object
smile.base.svm.SVR<T>
public class SVR<T> extends Object
Epsilon support vector regression. Like SVMs for classification, the model produced by SVR depends only on a subset of the training data, because the cost function ignores any training data close to the model prediction (within a threshold ε).

References

  1. A. J Smola and B. Scholkopf. A Tutorial on Support Vector Regression.
  2. Gary William Flake and Steve Lawrence. Efficient SVM Regression Training with SMO.
  3. Christopher J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2:121-167, 1998.
  4. John Platt. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines.
  5. Rong-En Fan, Pai-Hsuen, and Chih-Jen Lin. Working Set Selection Using Second Order Information for Training Support Vector Machines. JMLR, 6:1889-1918, 2005.
  6. Antoine Bordes, Seyda Ertekin, Jason Weston and Leon Bottou. Fast Kernel Classifiers with Online and Active Learning, Journal of Machine Learning Research, 6:1579-1619, 2005.
  7. Tobias Glasmachers and Christian Igel. Second Order SMO Improves SVM Online and Active Learning.
  8. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a Library for Support Vector Machines.

  • Constructor Details

    • SVR

      public SVR(MercerKernel<T> kernel, double eps, double C, double tol)
      Constructor.
      Parameters:
      kernel - the kernel function.
      eps - the loss function error threshold.
      C - the soft margin penalty parameter.
      tol - the tolerance of convergence test.

  • Method Details

    • fit

      public KernelMachine<T> fit(T[] x, double[] y)
      Fits an epsilon support vector regression model.
      Parameters:
      x - training instances.
      y - response variable.
      Returns:
      the model.