NDLI: Effect of choice of kernel in support vector machines on ambient air pollution forecasting

Content Provider	IEEE Xplore Digital Library
Author	Yang, J.Y. Ip, W.F. Vong, C.M. Wong, P.K.
Copyright Year	2011
Description	Author affiliation: Faculty of Science and Technology, University of Macau, China (Yang, J.Y.; Ip, W.F.; Vong, C.M.; Wong, P.K.)
Abstract	Forecasting of air pollution is a popular and important topic in recent year 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 practicians and 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 (SVM), 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.
Starting Page	552
Ending Page	557
File Size	490985
Page Count	6
File Format	PDF
ISBN	9781612843513
e-ISBN	9781612844718
e-ISBN	9781612844725
DOI	10.1109/ICSSE.2011.5961964
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Atmospheric modeling Predictive models Data models Mathematical model SVM Kernel Kernel Forecasting Support Vector Machines Pollution Level Forecasting
Content Type	Text
Resource Type	Article

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