Firstly, we use the idea of mapping by kernel function of KECA to transfer original global nonlinear problem into global linear one under the high-dimensional kernel feature space to improve the manifold learning dimension reduction algorithm LLE, then put the results obtained form KELE into RBF, constructing RBF model based on KELE. And we choose the foreign exchange rate time series to verify the improved RBF model, and the results show that the improved KELE can effectively reduce the dimension of samples and the prediction accuracy of the RBF model based on KELE is increased obviously.
| Published in | Science Journal of Business and Management (Volume 5, Issue 3) |
| DOI | 10.11648/j.sjbm.20170503.12 |
| Page(s) | 101-104 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Locally Liner Embedding, Kernel Entropy Component Analysis, Kernel Entropy Liner Embedding
| [1] | A. Babu and S. Reddy, "Exchange Rate Forecasting using ARIMA, Neural Network and Fuzzy Neuron," Journal of Stock & Forex Trading, vol. 2015, 2015. |
| [2] | J. Luo and L. Chen, "Realised Volatility Forecasts for Stock Index Futures Using the HAR Models with Bayesian Approaches," China Accounting and Finance Review, vol. 18, pp. 1-29, 2016. |
| [3] | R. Hafezi, J. Shahrabi, and E. Hadavandi, "A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price," Applied Soft Computing, vol. 29, pp. 196-210, 2015. |
| [4] | W. Zhang, C. Li, Y. Ye, W. Li, and E. W. Ngai, "Dynamic business network analysis for correlated stock price movement prediction," IEEE Intelligent Systems, vol. 30, pp. 26-33, 2015. |
| [5] | Ca'Zorzi M, Kocięcki A, Rubaszek M. Bayesian forecasting of real exchange rates with a Dornbusch prior [J]. Economic Modelling, 2015, 46: 53-60. |
| [6] | Li Q. How to forecast exchange rate, an unanswered puzzle [J]. 2010. |
| [7] | A. Babu and S. Reddy, "Exchange Rate Forecasting using ARIMA, Neural Network and Fuzzy Neuron," Journal of Stock & Forex Trading, vol. 2015, 2015. |
| [8] | R. Jenssen, "Kernel entropy component analysis," IEEE transactions on pattern analysis and machine intelligence, vol. 32, pp. 847-860, 2010. |
| [9] | B. Shekar, M. S. Kumari, L. M. Mestetskiy, and N. F. Dyshkant, "Face recognition using kernel entropy component analysis," Neurocomputing, vol. 74, pp. 1053-1057, 2011. |
| [10] | Y. Huang and G. Kou, "A kernel entropy manifold learning approach for financial data analysis," Decision Support Systems, vol. 64, pp. 31-42, 2014. |
APA Style
Chen Xiu-rong, Tian Yi-xiang. (2017). RBF Model Based on the Improved KELE Algorithm. Science Journal of Business and Management, 5(3), 101-104. https://doi.org/10.11648/j.sjbm.20170503.12
ACS Style
Chen Xiu-rong; Tian Yi-xiang. RBF Model Based on the Improved KELE Algorithm. Sci. J. Bus. Manag. 2017, 5(3), 101-104. doi: 10.11648/j.sjbm.20170503.12
AMA Style
Chen Xiu-rong, Tian Yi-xiang. RBF Model Based on the Improved KELE Algorithm. Sci J Bus Manag. 2017;5(3):101-104. doi: 10.11648/j.sjbm.20170503.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.sjbm.20170503.12,
author = {Chen Xiu-rong and Tian Yi-xiang},
title = {RBF Model Based on the Improved KELE Algorithm},
journal = {Science Journal of Business and Management},
volume = {5},
number = {3},
pages = {101-104},
doi = {10.11648/j.sjbm.20170503.12},
url = {https://doi.org/10.11648/j.sjbm.20170503.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20170503.12},
abstract = {Firstly, we use the idea of mapping by kernel function of KECA to transfer original global nonlinear problem into global linear one under the high-dimensional kernel feature space to improve the manifold learning dimension reduction algorithm LLE, then put the results obtained form KELE into RBF, constructing RBF model based on KELE. And we choose the foreign exchange rate time series to verify the improved RBF model, and the results show that the improved KELE can effectively reduce the dimension of samples and the prediction accuracy of the RBF model based on KELE is increased obviously.},
year = {2017}
}
TY - JOUR T1 - RBF Model Based on the Improved KELE Algorithm AU - Chen Xiu-rong AU - Tian Yi-xiang Y1 - 2017/05/04 PY - 2017 N1 - https://doi.org/10.11648/j.sjbm.20170503.12 DO - 10.11648/j.sjbm.20170503.12 T2 - Science Journal of Business and Management JF - Science Journal of Business and Management JO - Science Journal of Business and Management SP - 101 EP - 104 PB - Science Publishing Group SN - 2331-0634 UR - https://doi.org/10.11648/j.sjbm.20170503.12 AB - Firstly, we use the idea of mapping by kernel function of KECA to transfer original global nonlinear problem into global linear one under the high-dimensional kernel feature space to improve the manifold learning dimension reduction algorithm LLE, then put the results obtained form KELE into RBF, constructing RBF model based on KELE. And we choose the foreign exchange rate time series to verify the improved RBF model, and the results show that the improved KELE can effectively reduce the dimension of samples and the prediction accuracy of the RBF model based on KELE is increased obviously. VL - 5 IS - 3 ER -
Information
Email: