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A convex approach to validation-based learning of the regularization constant.

K Pelckmans, J A K Suykens, B De Moor

    IEEE Transactions on Neural Networks
    |May 29, 2007
    PubMed
    Summary
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    This study introduces a convex relaxation method for optimizing regularization constants in machine learning algorithms like ridge regression and LS-SVMs. This approach enhances computational efficiency and automation in learning methods.

    Area of Science:

    • Machine Learning
    • Computational Statistics

    Background:

    • Tuning regularization constants is crucial for model performance in various algorithms.
    • Existing methods can be computationally intensive and lack automation.

    Purpose of the Study:

    • To investigate a tight convex relaxation for optimizing regularization constants.
    • To improve the computational cost and automation of machine learning methods.

    Main Methods:

    • Developed a convex relaxation approach.
    • Applied the method to algorithms including ridge regression, regularization networks, smoothing splines, and least squares support vector machines (LS-SVMs).

    Main Results:

    • The convex relaxation provides a reliable and efficient computational framework.

    Related Experiment Videos

  • All solutions of the relaxation can be interpreted as solutions to a weighted LS-SVM.

    • Conclusions:

    • The proposed convex relaxation offers an efficient and automated approach to regularization constant tuning.
    • This method enhances the practical applicability of various regression techniques.