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

fYear

2007

fDate

5-8 Aug. 2007

Firstpage

844

Lastpage

849

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICMA.2007.4303655

Filename

4303655