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Two New Decomposition Algorithms for Training Bound-Constrained Support Vector Machines* Cover

Two New Decomposition Algorithms for Training Bound-Constrained Support Vector Machines*

Foundations of Computing and Decision Sciences
Volume 40 (2015): Issue 1 (March 2015)
By: ,  ,   and    
Open Access
|Mar 2015
References

References

  1. [1] Asuncion A., Newman D., UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences (2007) http://www. ics.uci.edu/~mlearn/MLRepository.html.
    Search in Google Scholar
  2. [2] Boser B., Guyon I., Vapnik V.A., A training algorithm for optimal margin classifiers,, ACM Press, 1992, 144-152.
    Open DOISearch in Google Scholar
  3. [3] Bottou L., Stochastic gradient descent examples, 2007, http://leon.bottou. org/projects/sgd.
    Search in Google Scholar
  4. [4] Cortes C., Vapnik V., Support-vector networks,, 20, 3, 1995, 273-297.
    Open DOISearch in Google Scholar
  5. [5] Franc V., Sonnenburg S., Optimized cutting plane algorithm for support vector machines, ICML 08:, ACM Press 2008, 320-327.
    Open DOISearch in Google Scholar
  6. [6] Gertz E., Wright S., Object-oriented software for quadratic programming,, 29, 2001, 58-81.
    Open DOISearch in Google Scholar
  7. [7] Hsieh C., Chang K., Li C.J., A comparison of methods for multi-class support vector machines,, 13, 2002, 415-425.
    Open DOISearch in Google Scholar
  8. [8] Hsieh C., Chang K., Lin C.J., Keerthi S., Sundararajan S., A dual coordinate descent method for large-scale linear SVM,, ACM, 2008, 408-415.
    Open DOISearch in Google Scholar
  9. [9] Hsu C.W., Lin C.J., A simple decomposition method for support vector machines,, 46, 1-3, 2002, 291{314.
    Open DOISearch in Google Scholar
  10. [10] Joachims T., SVMlight, http://svmlight.joachims.org/.
    Search in Google Scholar
  11. [11] Joachims T., Training linear SVMs in linear time,, 2006, 217-226.
    Open DOISearch in Google Scholar
  12. [12] Joachims T., Finley T., Yu C.N., Cutting-plane training of structural svms,, 77, 1, 2009, 27-59.
    Open DOISearch in Google Scholar
  13. [13] Joachims T., Yu C.N., Sparse kernel svms via cutting-plane training,, 76, 2-3, 2009, 179-193.
    Open DOISearch in Google Scholar
  14. [14] Mangasarian O., Musicant D., Successive overrelaxation for support vector machines,, 10, 5, 1999, 1032-1037.
    Open DOISearch in Google Scholar
  15. [15] Mangasarian O., Musicant D., Lagrangian support vector machines,, 1, 2001, 161-177.
    Search in Google Scholar
  16. [16] Mercer J., Functions of positive and negative type and their connection with the theory of integral equations,, 1909.
    Search in Google Scholar
  17. [17] Shalev-Shwartz S., Singer Y., Srebro N., Cotter A., Pegasos: Primal Estimated sub-Gradient Solber for SVM,, 127, 1, 2011, 3-30.
    Open DOISearch in Google Scholar
  18. [18] Sun W.Y., Yuan Y.X.,, Springer, New York, USA, 2006.
    Search in Google Scholar
  19. [19] Frieb T., Cristianini N., Campbell C., The kernel-adatron algorithm: a fast and simple learning procedure for support vector machines,, Morgan Kaufmann Publishers, 1998.
    Search in Google Scholar
  20. [20] Vapnik V.,, Springer-Verlag New York, Inc., New York, NY, USA 1995.
    Open DOISearch in Google Scholar
  21. [21] Vapnik V.,, Wiley- Interscience, September, 1998.
    Search in Google Scholar
  22. [22] Yuan G.X., Ho C.H., Lin C.J., Recent Advances of Large-scale Linear Classifi- cation,, 100, 2012, 2584-2603.
    Open DOISearch in Google Scholar
  23. [23] Zanni L., Serafini T., Zanghirati G., Parallel software for training large scale support vector machines on multiprocessor systems,, 7, 2006, 1467-1492.
    Search in Google Scholar
  24. [24] Osuna E., Freund R., Girosi F., Training support vector machines: An application to face detection,, 1997, 276-285.
    Search in Google Scholar
  25. [25] Arnosti N.A., Kalita J.K., Cutting Plane Training for Linear Support Vector Machines,, 25, 2013, 1186-1190.
    Open DOISearch in Google Scholar
  26. [26] Platt J.-C., Fast training of support vector machines using sequential minimal optimization, in: B. Scholkopf, C.J.C. Burges, A.J. Smola(Eds.),, MIT press, 1999, 185-208.
    Open DOISearch in Google Scholar
  27. [27] Joachims T., Making large-scale SVM learning practical, in: B. Scholkopf, C.J.C. Burges, A.J. Smola(Eds.),, Cambridge, MA, MIT press, 1998.
    Search in Google Scholar
  28. [28] Saunders C., Stitson M.O., Weston J., Bottou L., Scholkopf B., Smola A., Support vector machine reference manual, Egham, UK, Royal Holloway, University of London, Technical Report, NO.CSD-TR-98-03, 1998.
    Search in Google Scholar
  29. [29] Tian Y.J., Qi Z.Q., Ju X.C., Shi Y., Liu X.H., Nonparallel support vector machines for pattern classification,, 44, 7, 2013, 1067-1079.
    Open DOISearch in Google Scholar
  30. [30] Qi Z., Tian Y., Shi Y., Successive Overrelaxation for Lapiacian Support Vector Machine,, DOI: 10.1109/TNNLS.2014.2320738, 2014.
    Open DOISearch in Google Scholar
  31. [31] Qi Z., Tian Y., Shi Y., Structural Twin Support Vector Machine for Classification,, 43, 2013, 74-81.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.1515/fcds-2015-0005 | Journal eISSN: 2300-3405 (formerly 0867-6356) | Journal ISSN: 0867-6356
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Language: English
Page range: 67 - 86
Published on: Mar 1, 2015
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Keywords:
decomposition algorithm,
support vector machine,
quadratic programming,
global convergence
Related subjects:
Computer sciences,
Software development

© 2015 Lingfeng Niu, Ruizhi Zhou, Xi Zhao, Yong Shi, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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