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Support Vector Machines in R

Citations

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Cited by:

  1. Moro Russ A. & Härdle Wolfgang K. & Schäfer Dorothea, 2017. "Company rating with support vector machines," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 55-67, June.
  2. Sotiropoulou, Kalliopi F. & Vavatsikos, Athanasios P. & Botsaris, Pantelis N., 2024. "A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece," Renewable Energy, Elsevier, vol. 221(C).
  3. Liu, Zhen Jia, 2015. "Estudo cross-country sobre os fatores determinantes da crise financeira bancária," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 55(5), September.
  4. Ana Patrícia Rocha & Hugo Miguel Pereira Choupina & Maria do Carmo Vilas-Boas & José Maria Fernandes & João Paulo Silva Cunha, 2018. "System for automatic gait analysis based on a single RGB-D camera," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-24, August.
  5. Courage Kamusoko & Jonah Gamba & Hitomi Murakami, 2014. "Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers," Land, MDPI, vol. 3(2), pages 1-17, June.
  6. Gregory Gadzinski & Alessio Castello, 2022. "Combining white box models, black box machines and human interventions for interpretable decision strategies," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 17(3), pages 598-627, May.
  7. Na Tang & Maoxiang Yuan & Zhijun Chen & Jian Ma & Rui Sun & Yide Yang & Quanyuan He & Xiaowei Guo & Shixiong Hu & Junhua Zhou, 2023. "Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
  8. Perthame, Emeline & Forbes, Florence & Deleforge, Antoine, 2018. "Inverse regression approach to robust nonlinear high-to-low dimensional mapping," Journal of Multivariate Analysis, Elsevier, vol. 163(C), pages 1-14.
  9. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
  10. Tünde Takáts & László Pásztor & Mátyás Árvai & Gáspár Albert & János Mészáros, 2025. "Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards," Land, MDPI, vol. 14(1), pages 1-23, January.
  11. Phichhang Ou & Hengshan Wang, 2009. "Prediction of Stock Market Index Movement by Ten Data Mining Techniques," Modern Applied Science, Canadian Center of Science and Education, vol. 3(12), pages 1-28, December.
  12. Jindřich Špička, 2018. "How Do Agricultural Biogas Investments Affect Czech Farms?," Central European Business Review, Prague University of Economics and Business, vol. 2018(4), pages 34-60.
  13. Santos, Yan Antonino Costa & Rêgo, Leandro Chaves & Ospina, Raydonal, 2022. "Online handwritten signature verification via network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
  14. The-Hanh Pham & Jahmunah Vicnesh & Joel Koh En Wei & Shu Lih Oh & N. Arunkumar & Enas. W. Abdulhay & Edward J. Ciaccio & U. Rajendra Acharya, 2020. "Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals," IJERPH, MDPI, vol. 17(3), pages 1-15, February.
  15. Samia Zaoui & Clovis Foguem & Dieudonné Tchuente & Samuel Fosso-Wamba & Bernard Kamsu-Foguem, 2023. "The Viability of Supply Chains with Interpretable Learning Systems: The Case of COVID-19 Vaccine Deliveries," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 633-657, December.
  16. Sebastián Rodríguez & Pablo Cabrera-Barona, 2024. "A machine learning-based assessment of subjective quality of life," Journal of Computational Social Science, Springer, vol. 7(1), pages 451-467, April.
  17. Akın, Melda, 2015. "A novel approach to model selection in tourism demand modeling," Tourism Management, Elsevier, vol. 48(C), pages 64-72.
  18. Claudio Conversano & Elise Dusseldorp, 2017. "Modeling Threshold Interaction Effects Through the Logistic Classification Trunk," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 399-426, October.
  19. Vanessa Ress & Eva‐Maria Wild, 2024. "Comparing methods for estimating causal treatment effects of administrative health data: A plasmode simulation study," Health Economics, John Wiley & Sons, Ltd., vol. 33(12), pages 2757-2777, December.
  20. Oosterlinck, Dieter & Benoit, Dries F. & Baecke, Philippe, 2020. "From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1011-1024.
  21. Luca Longo, 2018. "Experienced mental workload, perception of usability, their interaction and impact on task performance," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-36, August.
  22. Paolo Sorino & Maria Gabriella Caruso & Giovanni Misciagna & Caterina Bonfiglio & Angelo Campanella & Antonella Mirizzi & Isabella Franco & Antonella Bianco & Claudia Buongiorno & Rosalba Liuzzi & Ann, 2020. "Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
  23. repec:cup:judgdm:v:17:y:2022:i:3:p:598-627 is not listed on IDEAS
  24. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.
  25. Christian Bunn & Peter Läderach & Oriana Ovalle Rivera & Dieter Kirschke, 2015. "A bitter cup: climate change profile of global production of Arabica and Robusta coffee," Climatic Change, Springer, vol. 129(1), pages 89-101, March.
  26. Benítez-Peña, Sandra & Blanquero, Rafael & Carrizosa, Emilio & Ramírez-Cobo, Pepa, 2024. "Cost-sensitive probabilistic predictions for support vector machines," European Journal of Operational Research, Elsevier, vol. 314(1), pages 268-279.
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