KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning

Tian, Kai, Guo, Ben, Liu, Guoqing, Mitchell, Ian ORCID logoORCID: https://orcid.org/0000-0002-3882-9127, Cheng, Dansong and Zhao, Wei (2013) KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning. Neurocomputing, 101 . pp. 24-31. ISSN 0925-2312 [Article] (doi:10.1016/j.neucom.2012.06.028)


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.

Item Type: Article
Keywords (uncontrolled): Bayesian Ying–Yang learning; CMAC; Kernel machine; Artificial Neural Networks
Research Areas: A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 11049
Useful Links:
Depositing User: Ian Mitchell
Date Deposited: 03 Jul 2013 04:54
Last Modified: 13 Oct 2016 14:27
URI: https://eprints.mdx.ac.uk/id/eprint/11049

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