APA (7th ed.) Citation

Deiss, L., Margenot, A. J., Culman, S. W., & Demyan, M. S. (2020). Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy. Geoderma, 365, 114227. https://doi.org/10.1016/j.geoderma.2020.114227

Chicago Style (17th ed.) Citation

Deiss, Leonardo, Andrew J. Margenot, Steve W. Culman, and M. Scott Demyan. "Tuning Support Vector Machines Regression Models Improves Prediction Accuracy of Soil Properties in MIR Spectroscopy." Geoderma 365 (2020): 114227. https://doi.org/10.1016/j.geoderma.2020.114227.

MLA (9th ed.) Citation

Deiss, Leonardo, et al. "Tuning Support Vector Machines Regression Models Improves Prediction Accuracy of Soil Properties in MIR Spectroscopy." Geoderma, vol. 365, 2020, p. 114227, https://doi.org/10.1016/j.geoderma.2020.114227.