Wang, W.J., Xu, Z.B., Lu, W.Z. and Zhang, X.Y. (2003) Determination of the Spread Parameter in the Gaussian Kernel for Classification and Regression. Neurocomputing, 55, 643-663.
http://dx.doi.org/10.1016/S0925-2312(02)00632-X

has been cited by the following article:

• TITLE: Comparison of Machine Learning Regression Methods to Simulate NO3 Flux in Soil Solution under Potato Crops

  AUTHORS: J. G. Fortin, A. Morais, F. Anctil, L. E. Parent

  KEYWORDS: Machine Learning Regression; Nitrate Leaching; Suction Lysimeter; Potato Cropping System

  JOURNAL NAME: Applied Mathematics, Vol.5 No.5, March 24, 2014

  ABSTRACT: Nitrate (NO3) leaching is a major issue in sandy soils intensively cropped to potato. Modelling could test the effect of management practices on nitrate leaching, particularly with regard to optimal N application rates. The NO3 concentration in the soil solution is well known for its local heterogeneity and hence represents a major challenge for modeling. The objective of this 2-year-study was to evaluate machine learning regression methods to simulate seasonal NO3 concentration dynamics in suction lysimeters in potato plots receiving different N application rates. Four machine learning function approximation methods were compared: multiple linear regressions, multivariate adaptive regression splines, multiple-layer perceptrons, and least squares support vector machines. Input candidates were chosen for known relationships with NO3 concentration. The best regression model was obtained with a 6-inputs least squares support vector machine combining cumulative rainfall, cumulative temperature, day of the year, N fertilisation rate, soil texture, and depth.