The nature of statistical learning theory
The nature of statistical learning theory
Multi-task feature and kernel selection for SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Forecasting of the daily meteorological pollution using wavelets and support vector machine
Engineering Applications of Artificial Intelligence
Least Squares Support Vector Prediction for Daily Atmospheric Pollutant Level
ICIS '10 Proceedings of the 2010 IEEE/ACIS 9th International Conference on Computer and Information Science
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.