smile.regression

Class SVR

java.lang.Object

smile.regression.SVR

public class SVR
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()

Method Summary

Modifier and Type	Method and Description
static Regression<double[]>	fit(double[][] x, double[] y, double eps, double C, double tol) Fits a linear epsilon-SVR.
static Regression<int[]>	fit(int[][] x, double[] y, int p, double eps, double C, double tol) Fits a linear epsilon-SVR of binary sparse data.
static Regression<smile.util.SparseArray>	fit(smile.util.SparseArray[] x, double[] y, int p, double eps, double C, double tol) Fits a linear epsilon-SVR of sparse data.
static <T> KernelMachine<T>	fit(T[] x, double[] y, smile.math.kernel.MercerKernel<T> kernel, double eps, double C, double tol) Fits a epsilon-SVR.

Methods inherited from class java.lang.Object

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

Constructor Detail

SVR

public SVR()

Method Detail

fit

public static Regression<double[]> fit(double[][] x,
                                       double[] y,
                                       double eps,
                                       double C,
                                       double tol)

Fits a linear epsilon-SVR.

Parameters:

x - training samples.

y - response variable.

eps - threshold parameter. There is no penalty associated with samples which are predicted within distance epsilon from the actual value. Decreasing epsilon forces closer fitting to the calibration/training data.

C - the soft margin penalty parameter.

tol - the tolerance of convergence test.

fit

public static Regression<int[]> fit(int[][] x,
                                    double[] y,
                                    int p,
                                    double eps,
                                    double C,
                                    double tol)

Fits a linear epsilon-SVR of binary sparse data.

Parameters:

x - training samples.

y - response variable.

eps - threshold parameter. There is no penalty associated with samples which are predicted within distance epsilon from the actual value. Decreasing epsilon forces closer fitting to the calibration/training data.

p - the dimension of input vector.

C - the soft margin penalty parameter.

tol - the tolerance of convergence test.

fit

public static Regression<smile.util.SparseArray> fit(smile.util.SparseArray[] x,
                                                     double[] y,
                                                     int p,
                                                     double eps,
                                                     double C,
                                                     double tol)

Fits a linear epsilon-SVR of sparse data.

Parameters:

x - training samples.

y - response variable.

eps - threshold parameter. There is no penalty associated with samples which are predicted within distance epsilon from the actual value. Decreasing epsilon forces closer fitting to the calibration/training data.

p - the dimension of input vector.

C - the soft margin penalty parameter.

tol - the tolerance of convergence test.

fit

public static <T> KernelMachine<T> fit(T[] x,
                                       double[] y,
                                       smile.math.kernel.MercerKernel<T> kernel,
                                       double eps,
                                       double C,
                                       double tol)

Fits a epsilon-SVR.

Parameters:

x - training samples.

y - response variable.

eps - threshold parameter. There is no penalty associated with samples which are predicted within distance epsilon from the actual value. Decreasing epsilon forces closer fitting to the calibration/training data.

kernel - the kernel function.

C - the soft margin penalty parameter.

tol - the tolerance of convergence test.