has been cited by the following article(s):
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[1]
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Smote vs. Random Undersampling for Imbalanced Data - Car Ownership Demand Model
Communications - Scientific letters of the University of Zilina,
2022
DOI:10.26552/com.C.2022.3.D105-D115
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[2]
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Smote vs. Random Undersampling for Imbalanced Data - Car Ownership Demand Model
Communications - Scientific letters of the University of Zilina,
2022
DOI:10.26552/com.C.2022.3.D105-D115
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[3]
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Smote vs. Random Undersampling for Imbalanced Data - Car Ownership Demand Model
Communications - Scientific letters of the University of Zilina,
2022
DOI:10.26552/com.C.2022.3.D105-D115
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[4]
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Smote vs. Random Undersampling for Imbalanced Data - Car Ownership Demand Model
Communications - Scientific letters of the University of Zilina,
2022
DOI:10.26552/com.C.2022.3.D105-D115
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[5]
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A Proof of Sparseness, Optimality, and Convergence of an LP-SVR
2020 International Conference on Computational Science and Computational Intelligence (CSCI),
2020
DOI:10.1109/CSCI51800.2020.00083
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[6]
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Prediction of asphaltene precipitation using support vector regression tuned with genetic algorithms
Petroleum,
2016
DOI:10.1016/j.petlm.2016.05.006
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[7]
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An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods
Pattern Recognition Letters,
2013
DOI:10.1016/j.patrec.2012.10.026
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