Subscribe Now Subscribe Today
Science Alert
Curve Top
Information Technology Journal
  Year: 2008 | Volume: 7 | Issue: 2 | Page No.: 320-325
DOI: 10.3923/itj.2008.320.325
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Multi-Resolution Signal Decomposition and Approximation Based on SVMS

Wang Liejun, Jia Zhenhong and Lu Zhaogan

Support Vector Machines (SVMs) and Multi-Resolution Analysis (MRA) both have been developed for solving signal approximation problem. Replacing the approximation criterion of MRA by which be used in SVMs, multi-resolution signal decomposition and approximation algorithm based on SVMs can be derived. The advantage of this algorithm not only reduces the approximation error by introducing structure risk, but also has better smoothness of approximation function. Experiment illustrates that this algorithm has better approximation performance than conventional MRA when applying it to the approximation of stationary signal.
PDF Fulltext XML References Citation Report Citation
  •    The Research of Ear Recognition Based on Gabor Wavelets and Support Vector Machine Classification
  •    Adaptive-Resonance-Theory Training Algorithm for Image Based on Single Training Example
  •    An Intrusion Detection Model Based on GS-SVM Classifier
  •    An Incremental Learning Approach with Support Vector Machine for Network Data Stream Classification Problem
How to cite this article:

Wang Liejun, Jia Zhenhong and Lu Zhaogan, 2008. Multi-Resolution Signal Decomposition and Approximation Based on SVMS. Information Technology Journal, 7: 320-325.

DOI: 10.3923/itj.2008.320.325






Curve Bottom