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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Support Function Machines
verfasst von : Jiuzhen Liang
Erschienen in: Intelligent Data Engineering and Automated Learning - IDEAL 2007
Verlag: Springer Berlin Heidelberg
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This paper proposes a novel model of support function machine (SFM) for time series predictions. Two machine learning models, namely, support vector machines (SVM) and procedural neural networks (PNN) are compared in solving time series and they inspire the creation of SFM. SFM aims to extend the support vectors to spatiotemporal domain, in which each component of vectors is a function with respect to time. In the view of the function, SFM transfers a vector function of time to a static vector. Similar to the SVM training procedure, the corresponding learning algorithm for SFM is presented, which is equivalent to solving a quadratic programming. Moreover, two practical examples are investigated and the experimental results illustrate the feasibility of SFM in modeling time series predictions.