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SMO (Sequential Minimal Optimization)

version (75.4 KB) by Bhartendu
Sequential Minimal Optimization (Simplified SMO) for SVM classification using Linear Kernel


Updated 24 May 2017

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*This demo is the implementation of the Algorithm in above-mentioned reference.
If we want to allow a variable threshold the updates must be made on a pair of data points, an approach that results in the SMO algorithm. The rate of convergence of the algorithm is strongly affected by the order in which the data points are chosen for updating. Heuristic measures such as the degree of violation of the KKT conditions can be used to ensure very effective convergence rates in practice.

Refer to: Platt, John. Fast Training of Support Vector Machines using Sequential Minimal Optimization,
in Advances in Kernel Methods – Support Vector Learning, B. Scholkopf, C. Burges,
A. Smola, eds., MIT Press (1998).

Cite As

Bhartendu (2022). SMO (Sequential Minimal Optimization) (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2016a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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