Package smile.regression

Class SVM

java.lang.Object

smile.regression.SVM

public class SVM
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.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final record	SVM.Options	SVM hyperparameters.

Method Summary

Modifier and Type	Method	Description
static Regression<double[]>	fit(double[][] x, double[] y, Properties params)	Fits an epsilon-SVR.
static Regression<double[]>	fit(double[][] x, double[] y, SVM.Options options)	Fits a linear epsilon-SVR.
static Regression<int[]>	fit(int[][] x, double[] y, int p, SVM.Options options)	Fits a linear epsilon-SVR of binary sparse data.
static Regression<SparseArray>	fit(SparseArray[] x, double[] y, int p, SVM.Options options)	Fits a linear epsilon-SVR of sparse data.
static <T> KernelMachine<T>	fit(T[] x, double[] y, MercerKernel<T> kernel, SVM.Options options)	Fits an epsilon-SVR.

Methods inherited from class java.lang.Object

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

Method Details

fit

public static Regression<double[]> fit(double[][] x,
 double[] y,
 SVM.Options options)

Fits a linear epsilon-SVR.

Parameters:

x - training samples.

y - response variable.

options - the hyperparameters.

Returns:

the model.

fit

public static Regression<int[]> fit(int[][] x,
 double[] y,
 int p,
 SVM.Options options)

Fits a linear epsilon-SVR of binary sparse data.

Parameters:

x - training samples.

y - response variable.

p - the dimension of input vector.

options - the hyperparameters.

Returns:

the model.

fit

public static Regression<SparseArray> fit(SparseArray[] x,
 double[] y,
 int p,
 SVM.Options options)

Fits a linear epsilon-SVR of sparse data.

Parameters:

x - training samples.

y - response variable.

p - the dimension of input vector.

options - the hyperparameters.

Returns:

the model.

fit

public static <T> KernelMachine<T> fit(T[] x,
 double[] y,
 MercerKernel<T> kernel,
 SVM.Options options)

Fits an epsilon-SVR.

Type Parameters:

T - the data type of samples.

Parameters:

x - training samples.

y - response variable.

kernel - the kernel function.

options - the hyperparameters.

Returns:

the model.

fit

public static Regression<double[]> fit(double[][] x,
 double[] y,
 Properties params)

Fits an epsilon-SVR.

Parameters:

x - training samples.

y - response variable.

params - the hyperparameters.

Returns:

the model.