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Updated: Jul 7, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Successive overrelaxation for support vector machines.

O L Mangasarian¹, D R Musicant

¹Computer Sciences Department, University of Wisconsin, Madison, WI 53706, USA.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This summary is machine-generated.

Successive overrelaxation (SOR) effectively trains support vector machines (SVMs) for massive datasets. This method processes large-scale data efficiently, outperforming other algorithms on big discrimination tasks.

Related Experiment Videos

Last Updated: Jul 7, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

Machine Learning
Optimization Algorithms

Background:

Training support vector machines (SVMs) on massive datasets presents significant computational challenges.
Conventional linear and quadratic programming methods struggle with datasets containing millions of points.

Purpose of the Study:

To introduce and evaluate the Successive Overrelaxation (SOR) algorithm for training SVMs on extremely large datasets.
To demonstrate SOR's capability in handling data that exceeds available memory.

Main Methods:

Utilized Successive Overrelaxation (SOR) for symmetric linear complementarity problems and quadratic programs to train SVMs.
Compared SOR's performance against Platt's Sequential Minimal Optimization (SMO) and Joachims' SVMlight algorithms.
Applied SOR to datasets with up to 10,000,000 points.

Main Results:

SOR successfully trained SVMs on massive datasets, processing one point at a time, thus not requiring data to reside in memory.
The algorithm demonstrated linear convergence to a solution.
Numerical results showed SOR's effectiveness on datasets up to 10 million points, solving problems intractable for other methods.
On smaller datasets, SOR was faster than SVMlight and comparable or faster than SMO.

Conclusions:

Successive Overrelaxation (SOR) offers a viable and efficient solution for training support vector machines on massive datasets.
SOR's ability to handle large-scale data makes it a significant advancement for discrimination problems previously unsolvable by conventional techniques.