KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning
Article
Tian, K., Guo, B., Liu, G., Mitchell, I., Cheng, D. and Zhao, W. 2013. KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning. Neurocomputing. 101, pp. 24-31. https://doi.org/10.1016/j.neucom.2012.06.028
Type | Article |
---|---|
Title | KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning |
Authors | Tian, K., Guo, B., Liu, G., Mitchell, I., Cheng, D. and Zhao, W. |
Abstract | The Cerebellar Model Articulation Controller (CMAC) possesses attractive properties of fast learning and simple computation. In application, the size of its association vector is always reduced to economize the memory requirement, greatly constraining its modeling capability. The kernel CMAC (KMAC), which provides an interpretation for the traditional CMAC from the kernel viewpoint, not only strengthens the modeling capability without increasing its complexity, but reinforces its generalization with the help of a regularization term. However, the KCMAC suffers from the problem of selecting its hyperparameter. In this paper, the Bayesian Ying–Yang (BYY) learning theory is incorporated into KCMAC, referred to as KCMAC-BYY, to optimize the hyperparameter. The proposed KCMAC-BYY achieves the systematic tuning of the hyperparameter, further improving the performance in modeling and generalization. The experimental results on some benchmark datasets show the prior performance of the proposed KCMAC-BYY to the existing representative techniques. |
Keywords | Bayesian Ying–Yang learning; CMAC; Kernel machine; Artificial Neural Networks |
Research Group | Artificial Intelligence group |
Publisher | Elsevier |
Journal | Neurocomputing |
ISSN | 0925-2312 |
Electronic | 1872-8286 |
Publication dates | |
Feb 2013 | |
Publication process dates | |
Deposited | 03 Jul 2013 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.neucom.2012.06.028 |
Web of Science identifier | WOS:000312171100004 |
Language | English |
https://repository.mdx.ac.uk/item/8423w
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