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- W2387482881 abstract "One of the bottlenecks restricting the development of fault diagnosis of turbogenerator unit is the lack of samples. Support vector machines(SVM) can achieve good performance when applied to small samples pattern recognition problems. A new learning architecture for multiclass support vector machines, DAGSVM, is analyzed. Based on number of error samples, an iterative algorithm is presented for the choice of the value of σ. DAGSVM is applied to fault diagnosis of turbogenerator unit. The results demonstrate that all of the test faults can be classified corrected and the validity of the algorithms is proved. Figs 2, tables 2 and refs 8." @default.
- W2387482881 created "2016-06-24" @default.
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- W2387482881 date "2003-01-01" @default.
- W2387482881 modified "2023-09-25" @default.
- W2387482881 title "Multi-Class Support Vector Machines for Fault Diagnosis of Turbogenerator Unit" @default.
- W2387482881 hasPublicationYear "2003" @default.
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