Please use this identifier to cite or link to this item:
|Title:||Online prediction of time series data with recurrent kernels||Authors:||Xu, Zhao
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2012||Source:||Xu, Z., Song, Q., Haijin, F., & Wang, D. (2012). Online prediction of time series data with recurrent kernels. The 2012 International Joint Conference on Neural Networks (IJCNN).||Abstract:||We propose a robust recurrent kernel online learning (RRKOL) algorithm which allows the exploitation of the kernel trick in an online fashion. The novel RRKOL algorithm achieves guaranteed weight convergence with regularized risk management through the recurrent hyper-parameters for a superior generalization performance. To select useful data to be learned and remove redundant ones, a sparcification procedure is developed based on the stability analysis of the system. Two time-series prediction examples are presented.||URI:||https://hdl.handle.net/10356/98301
|DOI:||http://dx.doi.org/10.1109/IJCNN.2012.6252747||Rights:||© 2012 IEEE.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Conference Papers|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.