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An incremental training method for the probabilistic RBF network.

Constantinos Constantinopoulos, Aristidis Likas

    IEEE Transactions on Neural Networks
    |July 22, 2006
    PubMed
    Summary
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    This study introduces an incremental training method for probabilistic radial basis function (PRBF) networks, enhancing classification performance. The new approach improves upon existing methods by dynamically adding components and outperforming hierarchical training and support vector machines.

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Probabilistic Radial Basis Function (PRBF) networks offer a probabilistic extension to RBF networks for classification.
    • Traditional PRBF training relies on the Expectation-Maximization (EM) algorithm, which is sensitive to initial parameter values.
    • Existing methods often use a fixed mixture model approach, limiting flexibility in complex classification tasks.

    Purpose of the Study:

    • To propose and evaluate an incremental training technique for PRBF networks in classification.
    • To develop an algorithm that dynamically adds components based on data space criticality.
    • To compare the proposed method's performance against hierarchical PRBF training and Support Vector Machines (SVM).

    Main Methods:

    Related Experiment Videos

  • An incremental algorithm that starts with a single component and adds more components strategically.
  • Component addition is guided by criteria identifying crucial regions in the data space for classification.
  • Post-component addition, each network component is split into class-specific subcomponents.

    • Main Results:

    • The incremental PRBF training method achieved superior classification performance on various datasets.
    • Experimental results demonstrated better outcomes compared to the hierarchical PRBF training approach.
    • Comparative analysis with Support Vector Machines showed competitive and qualitatively comparable results.

    Conclusions:

    • The proposed incremental training method offers a robust and effective alternative for PRBF network classification.
    • This approach mitigates the sensitivity to initial parameters often associated with traditional EM-based training.
    • The method shows promise for improving classification accuracy and efficiency in machine learning applications.