Parameter-insensitive kernel in extreme learning for non-linear support vector regression
(2011) Neurocomputing — Vol. 74, n° 16, p. 2526-2531 (2011)
Files
Parameter-insensitivekernelinextremelearningfornon-linearsupportvectorregressionpdf.pdf
Restricted Access - Adobe PDF
- 392.82 KB
Details
- Authors
- Abstract
- Support vector regression (SVR) is a state-of-the-art method for regression which uses the ε‐sensitive loss and produces sparse models. However, non-linear SVRs are difficult to tune because of the additional kernel parameter. In this paper, a new parameter-insensitive kernel inspired from extreme learning is used for non-linear SVR. Hence, the practitioner has only two meta-parameters to optimise. The proposed approach reduces significantly the computational complexity yet experiments show that it yields performances that are very close from the state-of-the-art. Unlike previous works which rely on Monte-Carlo approximation to estimate the kernel, this work also shows that the proposed kernel has an analytic form which is computationally easier to evaluate.
- Affiliations
Citations
Frénay, B., & Verleysen, M. (2011). Parameter-insensitive kernel in extreme learning for non-linear support vector regression. Neurocomputing, 74(16), 2526-2531. https://doi.org/10.1016/j.neucom.2010.11.037 (Original work published 2011)