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

Class BinaryNuSvmClassification

java.lang.Object

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

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

it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.BinaryNuSvmClassification

All Implemented Interfaces:

BinaryLearningAlgorithm, ClassificationLearningAlgorithm, KernelMethod, LearningAlgorithm

public class BinaryNuSvmClassification
extends LibNuSvmSolver
implements ClassificationLearningAlgorithm, KernelMethod

It implements the \(\nu\)-SVM learning algorithm discussed in [CC Chang & CJ Lin, 2011]. It is a learning algorithm for binary linear classification and 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.

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 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, logIteration, p, QD, shrinkingIteration, TAU, unshrink, y

Constructor Summary

Constructors

Constructor and Description
BinaryNuSvmClassification()
BinaryNuSvmClassification(Kernel kernel, Label label, float nu)

Method Summary

Methods

Modifier and Type	Method and Description
BinaryNuSvmClassification	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
float	getNu()
BinaryKernelMachineClassifier	getPredictionFunction() Returns the classifier learned during the training process
void	learn(Dataset trainingSet) 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	setNu(float nu)
void	setPredictionFunction(PredictionFunction predictionFunction) Sets the predictionFunction learned during the training process.

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

be_shrunk, calculate_r, calculate_rho, do_shrinking, select_working_set

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

get_QD, get_Qij, getCn, getCp, getEps, getKernel, getLabel, getLabels, info, is_free, is_lower_bound, is_upper_bound, kernel, reconstruct_gradient, setC, setCn, setCp, setEps, setLabel, setLabels, solve, swap_index, 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

Constructor Detail

BinaryNuSvmClassification

public BinaryNuSvmClassification()

BinaryNuSvmClassification

public BinaryNuSvmClassification(Kernel kernel,
                         Label label,
                         float nu)

Parameters:

kernel - The kernel function

label - The label to be learned

nu - The \(\nu\) parameter

Method Detail

duplicate

public BinaryNuSvmClassification 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

getNu

public float getNu()

Returns:

The \(\nu\) parameter

getPredictionFunction

public BinaryKernelMachineClassifier getPredictionFunction()

Description copied from interface: ClassificationLearningAlgorithm

Returns the classifier learned during the training process

Specified by:

getPredictionFunction in interface ClassificationLearningAlgorithm

Specified by:

getPredictionFunction in interface LearningAlgorithm

Returns:

the predictionFunction learned during the training process

learn

public void learn(Dataset trainingSet)

Description copied from interface: LearningAlgorithm

It starts the training process exploiting the provided dataset

Specified by:

learn in interface LearningAlgorithm

Parameters:

trainingSet - 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

setNu

public void setNu(float nu)

Parameters:

nu - The \(\nu\) parameter

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

setPredictionFunction

public void setPredictionFunction(PredictionFunction predictionFunction)

Description copied from interface: LearningAlgorithm

Sets the predictionFunction learned during the training process. It can be used to change the default prediction function, or to set an already trained one.

Specified by:

setPredictionFunction in interface LearningAlgorithm