On the convergence of the decomposition method for support vector machines

IEEE Transactions on Neural Networks

Volumn 12, Issue 6, 2001, Pages 1288-1298

  Lin, Chih Jen ^a,b  

^a IEEE   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Classification; Decomposition methods; Support vector machines (SVMs)

Indexed keywords

ASYMPTOTIC CONVERGENCE; DECOMPOSITION METHOD; SUPPORT VECTOR MACHINES;

ASYMPTOTIC STABILITY; CONVERGENCE OF NUMERICAL METHODS; OPTIMIZATION; SET THEORY; THEOREM PROVING; VECTORS;

ALGORITHMS;

EID: 0035506741     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.963765     Document Type: Article

References (35)

1. Uniqueness of the SVM solution
2. The analysis of decomposition methods for support vector machines
3. LIBSVM: A library for support vector machines
4. SVMTorch: A support vector machine for large-scale regression and classification problems
5. Support-vector network
6. Efficient SVM regression training with SMO
7. The kernel adatron algorithm: A fast and simple learning procedure for support vector machines
8. A simple decomposition method for support vector machines
9. Making large-scale SVM learning practical
10. Convergence of a generalized SMO algorithm for SVM classifier design
11. Improvements to Platt's SMO algorithm for SVM regression
12. Improvements to SMO algorithm for SVM classifier
13. An improved decomposition algorithm for regression support vector machines
14. Successive overrelaxation for support vector machines
15. Interpolation of scattered data: Distance matrices and conditionally positive definite functions
16. Training support vector machines: An application to face detection
17. Fast training of support vector machines using sequential minimal optimization
18. On search directions for minimization
19. mySVM - Another one of those support vector machines
20. New support vector algorithms
21. A tutorial on support vector regression
22. On the convergence of gradient methods under constraint
23. Training v-support vector classifiers: Theory and algorithms
24. A simple decomposition method for support vector machines
25. Convergence of a generalized SMO algorithm for SVM classifier design
26. Improvements to Platt's SMO algorithm for SVM classifier design
27. Improvements to SMO algorithm for SVM regression