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Related Experiment Video

Updated: Jul 14, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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

Summary

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.

Related Experiment Videos

Last Updated: Jul 14, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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.
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.