it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm

Class EpsilonSvmRegression

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver

it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver

it.uniroma2.sag.kelp.learningalgorithm.regression.libsvm.EpsilonSvmRegression

All Implemented Interfaces:

BinaryLearningAlgorithm, KernelMethod, LearningAlgorithm, RegressionLearningAlgorithm

public class EpsilonSvmRegression
extends LibCSvmSolver
implements RegressionLearningAlgorithm, KernelMethod

It implements the \(\epsilon\)-SVR learning algorithm discussed in [CC Chang & CJ Lin, 2011]. It is a learning algorithm for linear regression based on Support Vector Machines [Vapnik, 1998]. It relies on kernel functions.

It is a Java porting of the library LIBSVM v3.17, written in C++.

Further details can be found in:

[CC Chang & CJ Lin, 2011] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011.

[Vapnik, 1998] V. Vapnik. Statistical Learning Theory. Wiley, New York, NY, 1998.

and

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

Author:

Danilo Croce

Nested Class Summary

Nested classes/interfaces inherited from class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver

LibSvmSolver.Pair

Field Summary

Fields

Modifier and Type	Field and Description
protected UnivariateKernelMachineRegressionFunction	regressor The regression function to be returned

Fields inherited from class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver

active_set, active_size, alpha, alpha_status, cn, cp, doShrinking, eps, examples, G, G_bar, kernel, l, label, logIteration, p, QD, shrinkingIteration, TAU, unshrink, y

Constructor Summary

Constructors

Constructor and Description
EpsilonSvmRegression()
EpsilonSvmRegression(Kernel kernel, Label label, float c, float pReg)

Method Summary

Methods

Modifier and Type	Method and Description
EpsilonSvmRegression	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
protected float[]	get_QD() For each example i, it return the K_ii score
protected float	get_Qij(int i, int j)
RegressionFunction	getPredictionFunction() Returns the regressor learned during the training process
float	getpReg()
void	learn(Dataset dataset) It starts the training process exploiting the provided dataset
void	reset() Resets all the learning process, returning to the default state.
void	setKernel(Kernel kernel) Sets the kernel this
void	setLabels(Label... labels)
void	setpReg(float pReg)
protected void	swap_index(int i, int j) Swap the info of two examples

Methods inherited from class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibCSvmSolver

be_shrunk, calculate_rho, do_shrinking, getCSvmAlpha, select_working_set

Methods inherited from class it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver.LibSvmSolver

getCn, getCp, getKernel, getLabel, getLabels, info, is_free, is_lower_bound, is_upper_bound, kernel, reconstruct_gradient, setC, setCn, setCp, setLabel, setLabels, solve, swap, swap, swap, swap

Methods inherited from class java.lang.Object

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

Methods inherited from interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm

getLabels, setLabels

Methods inherited from interface it.uniroma2.sag.kelp.learningalgorithm.KernelMethod

getKernel

Field Detail

regressor

protected UnivariateKernelMachineRegressionFunction regressor

The regression function to be returned

Constructor Detail

EpsilonSvmRegression

public EpsilonSvmRegression(Kernel kernel,
                    Label label,
                    float c,
                    float pReg)

EpsilonSvmRegression

public EpsilonSvmRegression()

Method Detail

duplicate

public EpsilonSvmRegression duplicate()

Description copied from interface: LearningAlgorithm

Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated. A shallow copy of the learning parameters is performed, while the state parameters are initialized to their default value. The labels are not initialized neither copied

Specified by:

duplicate in interface LearningAlgorithm

get_QD

protected float[] get_QD()

Description copied from class: LibSvmSolver

For each example i, it return the K_ii score

Overrides:

get_QD in class LibSvmSolver

Returns:

the array of K_ii score

get_Qij

protected float get_Qij(int i,
            int j)

Overrides:

get_Qij in class LibSvmSolver

getpReg

public float getpReg()

setpReg

public void setpReg(float pReg)

getPredictionFunction

public RegressionFunction getPredictionFunction()

Description copied from interface: RegressionLearningAlgorithm

Returns the regressor learned during the training process

Specified by:

getPredictionFunction in interface LearningAlgorithm

Specified by:

getPredictionFunction in interface RegressionLearningAlgorithm

Returns:

the regressor learned during the training process

learn

public void learn(Dataset dataset)

Description copied from interface: LearningAlgorithm

It starts the training process exploiting the provided dataset

Specified by:

learn in interface LearningAlgorithm

Parameters:

dataset - the training data

reset

public void reset()

Description copied from interface: LearningAlgorithm

Resets all the learning process, returning to the default state. All the learned process is forgotten

Specified by:

reset in interface LearningAlgorithm

setKernel

public void setKernel(Kernel kernel)

Description copied from interface: KernelMethod

Sets the kernel this

Specified by:

setKernel in interface KernelMethod

Parameters:

kernel - the kernel to set

setLabels

public void setLabels(Label... labels)

swap_index

protected void swap_index(int i,
              int j)

Description copied from class: LibSvmSolver

Swap the info of two examples

Overrides:

swap_index in class LibSvmSolver

Parameters:

i - the first example index

j - the second example index