Title :
Implementation of SRM Principle Based on Wavelet Multi-resolution Approximation
Author :
Li, Yinguo ; Zhang, Liangfei ; Guo, Dongjin ; Shi, Yong
Author_Institution :
Chongqing Univ. of Posts & Telecommun., Chongqing
Abstract :
In statistical learning theory (SLT), structural risk minimization (SRM) of machine learning is hard to implement. Limitation for implementing SRM principle by computing the Vapnik-Chervonenkis (VC) dimension of function sets is analyzed. A novel idea is proposed to measure the learning capability of function sets by adopting high-frequency spectrum feature in the discrete wavelet base function set. An approach to implementation of SRM learning strategy is given, which is based on wavelet multi-resolution approximation, and a method for smoothing the learning surface are also put forward. Simulations indicate effectiveness of the proposed methods in the approximation of the noise-spoiled nonlinear signals.
Keywords :
approximation theory; learning (artificial intelligence); statistical analysis; wavelet transforms; SRM principle; Vapnik-Chervonenkis dimension; discrete wavelet base function set; function sets; high-frequency spectrum feature; learning surface; machine learning; noise-spoiled nonlinear signals; statistical learning theory; structural risk minimization; wavelet multiresolution approximation; Automation; Learning systems; Machine learning; Mechatronics; Neural networks; Risk management; Statistical learning; Support vector machines; Surface waves; Virtual colonoscopy; function learning; statistical learning theory; structural risk minimization (SRM); wavelet network;
Conference_Titel :
Mechatronics and Automation, 2007. ICMA 2007. International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-0828-3
Electronic_ISBN :
978-1-4244-0828-3
DOI :
10.1109/ICMA.2007.4303655
