A new strategy for selecting working sets applied in SMO

Proceedings - International Conference on Pattern Recognition

Volumn 16, Issue 3, 2002, Pages 427-430

  Li, Jianmin ^a   Zhang, Bo ^a   Lin, Fuzong ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BUFFER STORAGE; DATA STRUCTURES; OPTIMIZATION; PROBLEM SOLVING; STANDARDS; VECTORS; ALGORITHMS; SUPPORT VECTOR MACHINES;

KERNEL FUNCTIONS; SEQUENTIAL MINIMAL OPTIMIZATION (SMO); SUPPORT VECTOR MACHINES (SVM);

ALGORITHMS; PATTERN RECOGNITION;

FEASIBLE DIRECTION METHODS; KERNEL FUNCTION; LARGE-SCALE PROBLEM; NOVEL STRATEGIES; SEQUENTIAL MINIMAL OPTIMIZATION; SUPPORT VECTOR; TRAINING ALGORITHMS; TRAINING TIME;

EID: 28444491593     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. V.N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 2000.
2. C.J.C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, 1998, pp. 1-43.
3. K.P. Bennett, C. Campbell, "Support Vector Machines: Hype or Hallelujah?" SIGKDD Explorations, vol. 2, no. 2, 2000, pp.l-13.
4. E. Osuna, R. Freund, F. Girosi, "An Improved Training Algorithm for Support Vector Machines," Proc. 1997 IEEE Workshop Neural Networks for Signal Processing, IEEE, 1997, pp. 276-285.
5. T. Joachims, "Making Large-Scale Support Vector Machine Learning Practical," Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, Mass., 1999, pp. 169-184.
6. J.C. Platt. "Using Analytic QP and Sparseness to Speed Training of Support Vector Machines," Advances in Neural Information Processing Systems II, MIT Press, Cambridge, Mass., 1999, pp. 557-563.
7. S.S. Keerthi et al., "A Fast Iterative Nearest Point Algorithm for Support Vector Machine Classifier Design," IEEE Trans. Neural Network, vol. 11, no. 1, Jan. 2000, pp. 124-136.
8. C-C. Chang, C-J. Lin, "LIBSVM: a Library for Support Vector Machines (Version 2.3)," http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf (June 2001)
9. R. Collobert, S. Bengio, "SVMTorch: a Support Vector Machine for Large-Scale Regression and Classification Problems," Journal of Machine Learning Research, vol. 1, no. 1, 2001, pp. 143-160.
10. S.S. Keerthi, E.G. Gilbert, "Convergence of a Generalized SMO Algorithm for SVM Classifier Design," Machine Learning, vol. 46, nos. 1-3, Jan. 2002, pp. 351-360.
11. C-J. Lin, "Asymptotic Convergence of an SMO Algorithm without Any Assumptions," to be published in IEEE Trans. Neural Networks, 2002.
12. S.S. Keerthi et al, "Improvements to Plait's SMO Algorithm for SVM Classifier Design," Neural Computation, vol. 13, no. 3, Mar. 2001, pp. 637-649.
13. P. Laskov, "Feasible Direction Decomposition Algorithms for Training Support Vector Machines," Machine Learning, vol. 46, nos. 1-3, Jan. 2002, pp. 315-349.
14. G.C. Cawley, "Efficient Sequential Minimal Optimisation of Support Vector Classifiers," Proc. Int'l Conf. Artificial Neural Networks and Genetic Algorithms, Prague, Czech Republic, April 2001.
15. G.W. Flake, S. Lawrence, "Efficient SVM Regression Training with SMO," Machine Learning, vol. 46, nos. 1-3, Jan. 2002, pp. 271-290.
16. http://www.csie.ntu.edu.tw/-cjlin/libsvm/libsvm-2.31.zip (June 2001)
17. ftp://ftp-ai.cs.uni-dortmund.de/pub/Users/thorsten/svm_light/current/ svm_light.tar.gz (June 2001)
18. http://www.research.microsoft.com/~jplatt/adult.zip (June 2001)
19. http://www.research.microsoft.com/~jplatt/web.zip (June 2001)