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• W2054291723 abstract "The support vector machine (SVM) method, as a promising classification technique, has been widely used in various fields due to its high efficiency. However, SVM cannot effectively solve online classification problems since, when a new sample is misclassified, the classifier has to be retrained with all training samples plus the new sample, which is time consuming. According to the geometric characteristics of SVM, in this paper we propose an online SVM classifier called VS-OSVM, which is based on convex hull vertices selection within each class. The VS-OSVM algorithm has two steps: 1) the samples selection process, in which a small number of skeleton samples constituting an approximate convex hull in each class of the current training samples are selected and 2) the online updating process, in which the classifier is updated with newly arriving samples and the selected skeleton samples. From the theoretical point of view, the first <formula formulatype=inline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex Notation=TeX>$d+1$</tex></formula> ( <formula formulatype=inline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex Notation=TeX>$d$</tex></formula> is the dimension of the input samples) selected samples are proved to be vertices of the convex hull. This guarantees that the selected samples in our approach keep the greatest amount of information of the convex hull. From the application point of view, the new algorithm can update the classifier without reducing its classification performance. Experimental results on benchmark data sets have shown the validity and effectiveness of the VS-OSVM algorithm." @default.
• W2054291723 created "2016-06-24" @default.
• W2054291723 creator A5011877804 @default.
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• W2054291723 creator A5052304130 @default.
• W2054291723 date "2013-04-01" @default.
• W2054291723 modified "2023-09-27" @default.
• W2054291723 title "Online Support Vector Machine Based on Convex Hull Vertices Selection" @default.
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• W2054291723 doi "https://doi.org/10.1109/tnnls.2013.2238556" @default.
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