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A New SVM Multiclass Incremental Learning Algorithm
Joint Authors
Qin, Yuping
Li, Dan
Zhang, Aihua
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-05-21
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Qin, Yuping& Li, Dan& Zhang, Aihua. 2015. A New SVM Multiclass Incremental Learning Algorithm. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-5.
https://search.emarefa.net/detail/BIM-1074635
Modern Language Association (MLA)
Qin, Yuping…[et al.]. A New SVM Multiclass Incremental Learning Algorithm. Mathematical Problems in Engineering No. 2015 (2015), pp.1-5.
https://search.emarefa.net/detail/BIM-1074635
American Medical Association (AMA)
Qin, Yuping& Li, Dan& Zhang, Aihua. A New SVM Multiclass Incremental Learning Algorithm. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-5.
https://search.emarefa.net/detail/BIM-1074635
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074635