Title :
On the construction of support wavelet network
Author :
Gao, J. ; Chen, F. ; Shi, D.
Author_Institution :
Sch. of Math., Stat. & Comput. Sci., Univ. of New England, Armidale, NSW, Australia
Abstract :
Wavelet networks have emerged as a powerful tool for nonparametric estimation. It is a method implementing inverse discrete wavelet transform with coefficient optimization techniques from machine learning field. However, conventional ways to construct wavelet networks are based on empirical risk minimization (ERM) principle, which has been proven not as robust as structural risk minimization (SRM) principle. Thus, to explore the optimal architecture of wavelet networks, we constructed wavelet networks based on SRM principle. This paper describes the kernel-based way to optimize the architecture of wavelet networks. Based on the frame theory, wavelet kernel functions are found. After that, the wavelet network is constructed with support vectors generated by the wavelet kernel functions.
Keywords :
discrete wavelet transforms; learning (artificial intelligence); minimisation; coefficient optimization techniques; empirical risk minimization principle; inverse discrete wavelet transform; machine learning; nonparametric estimation; structural risk minimization principle; support wavelet network; Computer networks; Discrete wavelet transforms; Kernel; Neural networks; Power engineering and energy; Power engineering computing; Risk management; Robustness; Support vector machines; Training data;
Conference_Titel :
Systems, Man and Cybernetics, 2004 IEEE International Conference on
Print_ISBN :
0-7803-8566-7
DOI :
10.1109/ICSMC.2004.1400833
