Reference: http://cs229.stanford.edu/materials/smo.pdf
*This demo is the implementation of the Algorithm in above-mentioned reference.
SMO:
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).
인용 양식
Bhartendu (2024). SMO (Sequential Minimal Optimization) (https://www.mathworks.com/matlabcentral/fileexchange/63100-smo-sequential-minimal-optimization), MATLAB Central File Exchange. 검색됨 .
MATLAB 릴리스 호환 정보
플랫폼 호환성
Windows macOS Linux카테고리
태그
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!버전 | 게시됨 | 릴리스 정보 | |
---|---|---|---|
1.0.0.0 |