smile.regression

Class SVR.Trainer<T>

java.lang.Object

smile.regression.RegressionTrainer<T>

smile.regression.SVR.Trainer<T>

Enclosing class:

SVR<T>

public static class SVR.Trainer<T>
extends RegressionTrainer<T>

Trainer for support vector regression.

Constructor Summary

Constructors

Constructor and Description
SVR.Trainer(MercerKernel<T> kernel, double eps, double C) Constructor of trainer for binary SVMs.

Method Summary

Methods

Modifier and Type	Method and Description
void	setTolerance(double tol) Sets the tolerance of convergence test.
SVR<T>	train(T[] x, double[] y) Learns a regression model with given training data.

Methods inherited from class smile.regression.RegressionTrainer

setAttributes

Methods inherited from class java.lang.Object

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

Constructor Detail

SVR.Trainer

public SVR.Trainer(MercerKernel<T> kernel,
           double eps,
           double C)

Constructor of trainer for binary SVMs.

Parameters:

kernel - the kernel function.

eps - the loss function error threshold.

C - the soft margin penalty parameter.

Method Detail

setTolerance

public void setTolerance(double tol)

Sets the tolerance of convergence test.

Parameters:

tol - the tolerance of convergence test.

train

public SVR<T> train(T[] x,
           double[] y)

Description copied from class: RegressionTrainer

Learns a regression model with given training data.

Specified by:

train in class RegressionTrainer<T>

Parameters:

x - the training instances.

y - the training response values.

Returns:

a trained regression model.