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• W2885517796 abstract "To solve the problem that traditional multi-label support vector machine (SVM) classification algorithm adopting nonlinear kernel has been severely restricted from being used on large-scale data sets, we propose fast multi-label low-rank-linearized SVM classification algorithm based on approximate extreme points (AEML-LLSVM). First, it adopts the approximate extreme points' method to obtain representative sets from the training data set. Then, the approximate extreme points' low-rank-linearized SVM (AELLSVM) is trained on the representative sets. The AELLSVM integrates the advantages of approximate extreme points' method and LLSVM. Experimental results on three large-scale multi-label data sets have proven that the training and the testing speed of AEML-LLSVM classification algorithm are greatly improved under the premise that its classification performance is similar to that of ML-LIBSVM classification algorithm and superior to that of other fast multi-label SVM classification algorithms." @default.
• W2885517796 created "2018-08-22" @default.
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• W2885517796 date "2018-01-01" @default.
• W2885517796 modified "2023-09-26" @default.
• W2885517796 title "Fast Multi-Label Low-Rank Linearized SVM Classification Algorithm Based on Approximate Extreme Points" @default.
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• W2885517796 doi "https://doi.org/10.1109/access.2018.2854831" @default.
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