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Updated: Jul 7, 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

Pruning error minimization in least squares support vector machines.

B J de Kruif¹, T A de Vries

¹Drebbel Inst. for Mechatronics, Univ. of Twente, Enschede, Netherlands.

IEEE Transactions on Neural Networks

|February 2, 2008

Summary

A new pruning method for least squares support vector machines (LSSVM) improves function approximation accuracy. This approach reliably identifies training samples that minimize approximation errors upon omission, outperforming standard pruning techniques.

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Last Updated: Jul 7, 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:

Support Vector Machines (SVM) are used for classification and function approximation, often employing an epsilon-insensitive cost function.
Least Squares Support Vector Machines (LSSVM) utilize a quadratic cost function, typically yielding non-sparse solutions in function approximation tasks.

Purpose of the Study:

To introduce a novel pruning procedure for LSSVM that enhances the sparseness of the model.
To address the limitations of standard pruning methods in accurately predicting approximation errors after data omission.

Main Methods:

The study proposes a new pruning scheme for LSSVM that selects training samples based on their predicted impact on approximation error.
This method recursively solves the approximation problem and omits data, focusing on samples that minimize error upon removal.

Main Results:

The introduced pruning procedure demonstrates superior performance compared to the standard pruning method for LSSVM.
The new scheme reliably identifies training samples whose omission results in minimal approximation error.

Conclusions:

The proposed pruning strategy effectively enhances model sparseness in LSSVM function approximation.
This method offers a more reliable approach to data selection for pruning, leading to improved approximation accuracy.