The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Efficient SVM Regression Training with SMO
Machine Learning
Bio-support vector machines for computational proteomics
Bioinformatics
Complex number procedure neural networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
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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.