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Wavelet Network for Nonlinear Regression Using Probabilistic Framework

  • Shu-Fai Wong
  • Kwan-Yee Kenneth Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3174)

Abstract

Regression analysis is an essential tools in most research fields such as signal processing, economic forecasting etc. In this paper, an regression algorithm using probabilistic wavelet network is proposed. As in most neural network (NN) regression methods, the proposed method can model nonlinear functions. Unlike other NN approaches, the proposed method is much robust to noisy data and thus over-fitting may not occur easily. This is because the use of wavelet representation in the hidden nodes and the probabilistic inference on the value of weights such that the assumption of smooth curve can be encoded implicitly. Experimental results show that the proposed network have higher modeling and prediction power than other common NN regression methods.

Keywords

Hide Node Noisy Data Wavelet Function Mother Wavelet Probabilistic Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Shu-Fai Wong
    • 1
  • Kwan-Yee Kenneth Wong
    • 1
  1. 1.Department of Computer Science and Information SystemsThe University of Hong KongHong Kong

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