A New SVM Multiclass Incremental Learning Algorithm

A New SVM Multiclass Incremental Learning Algorithm

A new support vector machine (SVM) multiclass incremental learning algorithm is proposed. To each class training sample, the hyperellipsoidal classifier that includes as many samples as possible and pushes the outlier samples away is trained in the feature space. When the new samples are added to th...

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Main Authors: Yuping Qin, Dan Li, Aihua Zhang
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/745815
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spelling doaj-b5fceec3d7f94683918139c69d90b1062020-11-24T22:23:55ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/745815745815A New SVM Multiclass Incremental Learning AlgorithmYuping Qin0Dan Li1Aihua Zhang2College of Engineering, Bohai University, Jinzhou 121013, ChinaCollege of Mathematics and Physics, Bohai University, Jinzhou 121013, ChinaCollege of Engineering, Bohai University, Jinzhou 121013, ChinaA new support vector machine (SVM) multiclass incremental learning algorithm is proposed. To each class training sample, the hyperellipsoidal classifier that includes as many samples as possible and pushes the outlier samples away is trained in the feature space. When the new samples are added to the classification system, the algorithm reuses the old classifiers that have nothing to do with the new sample classes. To be classified sample, the Mahalanobis distances are used to decide the class of classified sample. If the sample point is not surrounded by any hyperellipsoidal or is surrounded by more than one hyperellipsoidal, the membership is used to confirm its class. The experimental results show that the algorithm has higher performance in classification precision and classification speed.http://dx.doi.org/10.1155/2015/745815
collection DOAJ
language English
format Article
sources DOAJ
author Yuping Qin
Dan Li
Aihua Zhang
spellingShingle Yuping Qin
Dan Li
Aihua Zhang
A New SVM Multiclass Incremental Learning Algorithm
Mathematical Problems in Engineering
author_facet Yuping Qin
Dan Li
Aihua Zhang
author_sort Yuping Qin
title A New SVM Multiclass Incremental Learning Algorithm
title_short A New SVM Multiclass Incremental Learning Algorithm
title_full A New SVM Multiclass Incremental Learning Algorithm
title_fullStr A New SVM Multiclass Incremental Learning Algorithm
title_full_unstemmed A New SVM Multiclass Incremental Learning Algorithm
title_sort new svm multiclass incremental learning algorithm
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2015-01-01
description A new support vector machine (SVM) multiclass incremental learning algorithm is proposed. To each class training sample, the hyperellipsoidal classifier that includes as many samples as possible and pushes the outlier samples away is trained in the feature space. When the new samples are added to the classification system, the algorithm reuses the old classifiers that have nothing to do with the new sample classes. To be classified sample, the Mahalanobis distances are used to decide the class of classified sample. If the sample point is not surrounded by any hyperellipsoidal or is surrounded by more than one hyperellipsoidal, the membership is used to confirm its class. The experimental results show that the algorithm has higher performance in classification precision and classification speed.
url http://dx.doi.org/10.1155/2015/745815
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AT danli newsvmmulticlassincrementallearningalgorithm
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