Ensemble Radical Basis Function Neural Networks for Regression Based on Statistical Learning Theory
- 10.2991/icem-17.2018.71How to use a DOI?
- Statistical learning theory; Radical basis function neural networks; VC dimension; Ensemble learning
We proposed an algorithm to construct ensemble radical basis function neural networks for regression estimation. Taking full advantage of the characteristic of radial basis function, we calculated groups of approximate basis in Reproducing Kernel Hilbert Space (RKHS). The approximate basis could be used to represent all the samples by the way of linear combination. By this way, the weak learners of radial basis function neural network were built. But it was proved that the weak learners were not accurate enough. In order to get accurate and stable learning machine with better generalization ability, we proposed the Ensemble Radical Basis Function Neural Networks (ERBFNNs). Employing the sinc function, the proposed ERBFNNs have shown exciting outcomes as have come out at the end of the paper.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Liangzhi Gan AU - Dawei Jiang AU - Mi He PY - 2018/01 DA - 2018/01 TI - Ensemble Radical Basis Function Neural Networks for Regression Based on Statistical Learning Theory BT - Proceedings of the 2017 7th International Conference on Education and Management (ICEM 2017) PB - Atlantis Press SP - 352 EP - 355 SN - 2352-5428 UR - https://doi.org/10.2991/icem-17.2018.71 DO - 10.2991/icem-17.2018.71 ID - Gan2018/01 ER -