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- W67382679 abstract "Lagrangian support vector machine (LSVM) owns good generalization ability. But traditional LSVM regards all attributes of samples owning the same important degree in training process. In fact, some attributes are more important than others for training LSVM. In this issue, a novel LSVM is proposed. The different weight parameter for the different attribute is obtained by particle swarm optimization. The crane gear fault diagnosis experiment is accomplished to demonstrate the effect of the novel LSVM." @default.
- W67382679 created "2016-06-24" @default.
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- W67382679 date "2012-01-01" @default.
- W67382679 modified "2023-10-03" @default.
- W67382679 title "A Novel Lagrangian Support Vector Machine and Application in the Crane Gear Fault Diagnosis System" @default.
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- W67382679 doi "https://doi.org/10.1007/978-3-642-31507-7_60" @default.
- W67382679 hasPublicationYear "2012" @default.
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