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Related Experiment Video

Updated: Jun 29, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

A new solution path algorithm in support vector regression.

Gang Wang¹, Dit-Yan Yeung, Frederick H Lochovsky

¹Hong Kong University of Science and Technology, Hong Kong, China. gawa@microsoft.com

IEEE Transactions on Neural Networks

|October 10, 2008

Summary

This summary is machine-generated.

This study introduces a new epsilon-path algorithm for support vector regression (SVR) model selection. This method efficiently explores solutions by tracing the epsilon hyperparameter, offering practical advantages over existing lambda-path algorithms.

Related Experiment Videos

Last Updated: Jun 29, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Area of Science:

Machine Learning
Computational Statistics

Background:

Regularization path algorithms offer efficient model selection by exploring solution spaces.
Support Vector Regression (SVR) models, including epsilon-SVR, have been adapted for this approach.
Current methods require pre-specification of the error parameter epsilon, limiting flexibility.

Purpose of the Study:

To analyze the solution space of epsilon-SVR.
To propose a novel epsilon-path algorithm for SVR model selection.
To overcome limitations of existing lambda-path algorithms.

Main Methods:

Analysis of the epsilon-SVR solution space.
Development of a new solution path algorithm tracing the epsilon hyperparameter.
Comparison of the proposed epsilon-path algorithm with the lambda-path algorithm.

Main Results:

The epsilon-path algorithm effectively traces the solution path with respect to epsilon.
Both epsilon-path and lambda-path algorithms exhibit piecewise linearity.
The epsilon-path algorithm demonstrates advantages over the lambda-path algorithm.

Conclusions:

The proposed epsilon-path algorithm provides a more flexible and practical approach to SVR model selection.
This method enhances the utility of regularization path algorithms for epsilon-SVR.
The algorithm's advantages make it appealing for real-world applications.