Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy - Deiss, Leonardo; Margenot, Andrew J.; Culman, Steve W.; Demyan, M. Scott (Geoderma, 2020)
Keywords: chemometrics; clay; data collection; Error-grid; Fourier transform infrared spectroscopy; FTIR; Kernel; land use; least squares; Machine-learning; Midwestern United States; nonlinear models; prediction; RMSE; sand; soil properties; statistical models; support vector machines; Tanzania; total organic carbon; Midwestern United States; Tanzania; Error-grid; RMSE; Machine-learning; Kernel; FTIR