Open Access
2014 A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
Yuping Qin, Hamid Reza Karimi, Dan Li, Shuxian Lun, Aihua Zhang
Abstr. Appl. Anal. 2014: 1-5 (2014). DOI: 10.1155/2014/894246


A Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space. In the process of incremental learning, only one subclassifier is trained with the new class samples. The old models of the classifier are not influenced and can be reused. In the process of classification, considering the information of sample’s distribution in the feature space, the Mahalanobis distances from the sample mapping to the center of each hyperellipsoidal are used to decide the classified sample class. The experimental results show that the proposed method has higher classification precision and classification speed.


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Yuping Qin. Hamid Reza Karimi. Dan Li. Shuxian Lun. Aihua Zhang. "A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm." Abstr. Appl. Anal. 2014 1 - 5, 2014.


Published: 2014
First available in Project Euclid: 2 October 2014

zbMATH: 07023256
MathSciNet: MR3170415
Digital Object Identifier: 10.1155/2014/894246

Rights: Copyright © 2014 Hindawi

Vol.2014 • 2014
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