smile.base.svm

Class SVR<T>

java.lang.Object

smile.base.svm.SVR<T>

public class SVR<T>
extends java.lang.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 Summary

Constructors

Constructor and Description
SVR(smile.math.kernel.MercerKernel<T> kernel, double eps, double C, double tol) Constructor.

Method Summary

Modifier and Type	Method and Description
KernelMachine<T>	fit(T[] x, double[] y) Fits a epsilon support vector regression model.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SVR

public SVR(smile.math.kernel.MercerKernel<T> kernel,
           double eps,
           double C,
           double tol)

Constructor.

Parameters:

kernel - the kernel function.

Method Detail

fit

public KernelMachine<T> fit(T[] x,
                            double[] y)

Fits a epsilon support vector regression model.

Parameters:

x - training instances.

y - response variable.