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A mahalanobis hyperellipsoidal learning machine class incremental learning algorithm

Qin, Yuping; Karimi, Hamid Reza; Li, Dan; Lun, Shuxian; Zhang, Aihua
Journal article, Peer reviewed
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URI
http://hdl.handle.net/11250/227712
Date
2014
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  • Scientific Publications in Engineering Sciences [820]
Original version
Qin, Y., Karimi, H. R., Li, D., Lun, S., & Zhang, A. (2014). A mahalanobis hyperellipsoidal learning machine class incremental learning algorithm. Abstract and Applied Analysis, 2014, 1-5. doi: 10.1155/2014/894246   10.1155/2014/894246
Abstract
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.
Description
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/894246 Open Access
Publisher
Hindawi Publishing Corporation
Journal
Abstract and Applied Analysis

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