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Hierarchical bayesian models for regularisation in sequential learning

de Freitas JF1, M Niranjan M, Gee

  • 1Cambridge University Engineering Department, Cambridge CB2 1PZ, U.K.

Neural Computation
|April 19, 2000
PubMed
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This study introduces a hierarchical Bayesian modeling approach for regularization in sequential learning, overcoming limitations of traditional methods. The Bayesian framework with extended Kalman filtering enables effective regularization and adaptive noise estimation for online learning.

Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Signal Processing

Background:

  • Sequential learning environments pose challenges for traditional regularization and model selection techniques like cross-validation.
  • Online learning requires methods that can adapt and regularize as new data arrives incrementally.

Purpose of the Study:

  • To present a hierarchical Bayesian modeling approach for regularization in sequential learning.
  • To demonstrate the capability of this approach to handle model selection, parameter estimation, and noise estimation in dynamic environments.

Main Methods:

  • Hierarchical Bayesian modeling framework.
  • Extended Kalman filtering for parameter estimation within a minimum variance context.
  • Multilayer perceptron for nonlinear measurement mapping in the Extended Kalman Filter.

Related Experiment Videos

  • Novel algorithms for on-line noise estimation to achieve regularization.

    • Main Results:

    • The proposed Bayesian approach facilitates regularization in sequential learning, a task difficult for standard methods.
    • The framework integrates model selection, parameter estimation, and noise estimation across three inference levels.
    • Theoretical connections are established between adaptive noise estimation, multiple adaptive learning rates, and regularization coefficients within the Extended Kalman Filter.

    Conclusions:

    • Hierarchical Bayesian modeling offers a robust solution for regularization in sequential learning scenarios.
    • The integration of Extended Kalman filtering and adaptive noise estimation provides a powerful framework for online learning and adaptation.