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

Class OneClassSvmClassification

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.classification.libsvm.BinaryCSvmClassification

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

All Implemented Interfaces:

BinaryLearningAlgorithm, ClassificationLearningAlgorithm, KernelMethod, LearningAlgorithm

public class OneClassSvmClassification
extends BinaryCSvmClassification

It implements the One class SVM learning algorithm discussed in [CC Chang & CJ Lin, 2011]. It is a learning algorithm for estimating the Support of a High-Dimensional Distribution and it relies on kernel functions. The model is acquired only by considering positive examples. It is useful in anomaly detection (a.k.a. novelty detection)

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.BinaryCSvmClassification

classifier

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
OneClassSvmClassification()
OneClassSvmClassification(Kernel kernel, Label label, float nu)

Method Summary

Methods

Modifier and Type	Method and Description
OneClassSvmClassification	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
float	getNu()
void	learn(Dataset trainingSet) It starts the training process exploiting the provided dataset
void	setNu(float nu)

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

getPredictionFunction, isFairness, reset, setFairness, setKernel

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

get_QD, get_Qij, getCn, getCp, getKernel, getLabel, getLabels, info, is_free, is_lower_bound, is_upper_bound, kernel, reconstruct_gradient, setC, setCn, setCp, 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

OneClassSvmClassification

public OneClassSvmClassification()

OneClassSvmClassification

public OneClassSvmClassification(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 OneClassSvmClassification 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

Overrides:

duplicate in class BinaryCSvmClassification

getNu

public float getNu()

Returns:

the \(\nu\) parameter

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

Overrides:

learn in class BinaryCSvmClassification

Parameters:

trainingSet - the training data

setNu

public void setNu(float nu)

Parameters:

nu - the \(\nu\) parameter