2012
DOI: 10.1002/nag.2154
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Modeling of tensile strength of rocks materials based on support vector machines approaches

Abstract: In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS-SVM) provides a computational advantage over SVM by converting quadratic optimization… Show more

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Cited by 31 publications
(13 citation statements)
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“…ese statistical parameters can reflect the accuracy of predicting results. e calculation formulas of RMSE, MAPE, and R2 have been given in literature [49,50].…”
Section: Svm Model Preparation and Preprocessingmentioning
confidence: 99%
“…ese statistical parameters can reflect the accuracy of predicting results. e calculation formulas of RMSE, MAPE, and R2 have been given in literature [49,50].…”
Section: Svm Model Preparation and Preprocessingmentioning
confidence: 99%
“…(b: bias terimi, y k : ölçülen değer; ŷ k : SVM/ LS-SVM modeli çıktı değeri, w: ağırlık vektörü, M:marj). Figure 2. a)The optimal separator canonical hyperplane having the most margin (Tolun 2008), b)insensitive band for a one-dimensional linear regression mode (Tolun, 2008), c) insensitive loss function of SVM (Ceryan et al, 2012) and d) quadratic loss function of LS-SVM (Ceryan et al, 2012 (b: …”
Section: En Küçük Karaler Destek Vektör Maki̇neleri̇ Modeli̇ (Ls-svm)unclassified
“…Figure 2. a)The optimal separator canonical hyperplane having the most margin (Tolun 2008), b)insensitive band for a one-dimensional linear regression mode (Tolun, 2008), c) insensitive loss function of SVM (Ceryan et al, 2012) and d) quadratic loss function of LS-SVM (Ceryan et al, 2012 (b: the bias term, y k : the parameter value measured, ŷ k : the output of SVM/LS-SVM models, w: weight vector, M: Margin).…”
Section: (9)mentioning
confidence: 99%
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“…We also like to mention that transformation through Equation (11) is also termed as "normalization" in many studies (e.g. Ceryan et al 2013;Ismail et al 2012;Kisi 2012;Samsudin et al 2011). Thus, the word "normalization" was used in BM2013.…”
mentioning
confidence: 99%