Design of specific-to-problem kernels and use of kernel weighted K-nearest neighbours for time series modelling

@article{Rubio2010DesignOS,
  title={Design of specific-to-problem kernels and use of kernel weighted K-nearest neighbours for time series modelling},
  author={Gin{\'e}s Rubio and Luis Javier Herrera and H{\'e}ctor Pomares and Ignacio Rojas and Alberto Guill{\'e}n},
  journal={Neurocomputing},
  year={2010},
  volume={73},
  pages={1965-1975}
}
Least squares support vector machines (LSSVM) with Gaussian kernel represent the most used of the kernel methods existing in the literature for regression and time series prediction. These models have a good behaviour for these types of problems due to their generalization capabilities and their smooth interpolation, but they are very dependent on the feature selection performed and their computational cost notably increases with the number of training samples. Time series prediction can be… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 31 references

Use of specific-toproblem kernel functions for time series modeling

G. Rubio, A. Guillen, L. J. Herrera, H. Pomares, I. Rojas
in: ESTSP’08: Proceedings of the European Symposium on Time Series Prediction • 2008
View 4 Excerpts
Highly Influenced

Variable Neighborhood Search

N Mladenovir, E Hansen 't
-1
View 7 Excerpts
Highly Influenced

Gaussian Processes for Machine Learning

Advanced Lectures on Machine Learning • 2009
View 8 Excerpts
Highly Influenced

Learning with Kernels: support vector machines, regularization, optimization, and beyond

Adaptive computation and machine learning series • 2002
View 3 Excerpts
Highly Influenced

A new interface for mpi in matlab and its application over a genetic algorithm

A. Guillen, I. Rojas, +3 authors J. Gonzalez
in: Proceedings of the European Symposium on Time Series Prediction • 2008
View 1 Excerpt

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