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V. Vapnik, S. Golowich and A. Smola, “Support Method for Function Approximation Regression Estimation, and Signal Processing. Advances in Neural Information Processing Systems,” MIT Press, Cambridge, 1997.

has been cited by the following article:

• TITLE: Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach

  AUTHORS: Artemio Sotomayor-Olmedo, Marco A. Aceves-Fernández, Efrén Gorrostieta-Hurtado, Carlos Pedraza-Ortega, Juan M. Ramos-Arreguín, J. Emilio Vargas-Soto

  KEYWORDS: Predictive Models; Airborne Pollution; Support Vector Machines; Kernel Functions

  JOURNAL NAME: International Journal of Intelligence Science, Vol.3 No.3, July 5, 2013

  ABSTRACT: The development of forecasting models for pollution particles shows a nonlinear dynamic behavior; hence, implementation is a non-trivial process. In the literature, there have been multiple models of particulate pollutants, which use softcomputing techniques and machine learning such as: multilayer perceptrons, neural networks, support vector machines, kernel algorithms, and so on. This paper presents a prediction pollution model using support vector machines and kernel functions, which are: Gaussian, Polynomial and Spline. Finally, the prediction results of ozone (O3), particulate matter (PM10) and nitrogen dioxide (NO2) at Mexico City are presented as a case study using these techniques.