An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Sparse on-line Gaussian processes
Neural Computation
Sparse Online Greedy Support Vector Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
On incorporating seasonal information on recursive time series predictors
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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In this paper we report the results obtained using a kernel-based approach to predict the temporal development of four response signals in the process control of a glass melting tank with 16 input parameters. The data set is a revised version from the modelling challenge in EUNITE-2003. The central difficulties are: large time-delays between changes in the inputs and the outputs, large number of data, and a general lack of knowledge about the relevant variables that intervene in the process. The methodology proposed here comprises Support Vector Machines (SVM) and Regularization Networks (RN). We use the idea of sparse approximation both as a means of regularization and as a means of reducing the computational complexity. Furthermore, we will use an incremental approach to add new training examples to the kernel-based method and efficiently update the current solution. This allows us to use a sophisticated learning scheme, where we iterate between prediction and training, with good computational efficiency and satisfactory results.