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1 Erratum to: Neural Comput & Applic DOI 10.1007/s00521-015-1909-9
In the original publication, some of the equations and Fig. 6 have been cited incorrectly.
In Sect. 3.2, paragraph four, the article states:
“In Eq. 4, β is the matrix of output weight and Y is the matrix of class label, which can be expressed, respectively, as”
Correction: This should read as, “In Eq. 5, β is the matrix of output weight and Y is the matrix of class label, which can be expressed, respectively, as”
In Sect. 3.2, paragraph five, the article states:
“Therefore, the training process of ELM is equivalent to solve the linear Eq. 4, and the output weights β can be estimated”
Correction: This text should refer Eq. 5 as “Therefore, the training process of ELM is equivalent to solve the linear Eq. 5, and the output weights β can be estimated”
In Sect. 3.2, paragraph 6, the article states:
“It is worth noting that because the RTSSP is a binary classification problem, we consider there is only one node in the output layer, and the output dimension m is set to 1 in Eq. 8 and hereafter.”
Correction: This text should refer Eq. 9 as, “It is worth noting that because the RTSSP is a binary classification problem, we consider there is only one node in the output layer, and the output dimension m is set to 1 in Eq. 9 and hereafter.”
In Sect. 3.3, paragraph two, the article states:
“The Lagrangian function associated with the equality constrained problem of Eq. 9 is”
Correction: This should read as “The Lagrangian function associated with the equality constrained problem of Eq. 10 is”
In Sect. 5.3, paragraph two, the article states:
As can be seen in Fig. 6, cost-sensitive methods are more suitable than cost-blind methods since they achieve lower Costs and FDR.
Correction: This should refer to Fig. 7.
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The online version of the original article can be found under doi:10.1007/s00521-015-1909-9.
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Chen, Z., Xiao, X., Li, C. et al. Erratum to: Real-time transient stability status prediction using cost-sensitive extreme learning machine. Neural Comput & Applic 27, 333 (2016). https://doi.org/10.1007/s00521-015-1926-8
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DOI: https://doi.org/10.1007/s00521-015-1926-8