RBF Model Based on the Improved KELE Algorithm
Science Journal of Business and Management
Volume 5, Issue 3, June 2017, Pages: 101-104
Received: May 4, 2017; Published: May 4, 2017
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Authors
Chen Xiu-rong, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China
Tian Yi-xiang, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China
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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.
Keywords
Locally Liner Embedding, Kernel Entropy Component Analysis, Kernel Entropy Liner Embedding
To cite this article
Chen Xiu-rong, Tian Yi-xiang, RBF Model Based on the Improved KELE Algorithm, Science Journal of Business and Management. Vol. 5, No. 3, 2017, pp. 101-104. doi: 10.11648/j.sjbm.20170503.12
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