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Volumn 7, Issue , 2006, Pages 1467-1492

Parallel software for training large scale support vector machines on multiprocessor systems

Author keywords

Decomposition techniques; Gradient projection methods; Large scale quadratic programs; Parallel computation; Support vector machines

Indexed keywords

BENCHMARKING; CODES (SYMBOLS); COMPUTATION THEORY; COMPUTER SOFTWARE; ITERATIVE METHODS; MULTIPROCESSING SYSTEMS; PROBLEM SOLVING; QUADRATIC PROGRAMMING;

EID: 33745784205     PISSN: 15337928     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (94)

References (37)
  • 2
    • 0003713964 scopus 로고    scopus 로고
    • Athena Scientific, Belmont, MA, second edition
    • Dimitri P. Bertsekas. Nonlinear Programming. Athena Scientific, Belmont, MA, second edition, 1999.
    • (1999) Nonlinear Programming
    • Bertsekas, D.P.1
  • 3
    • 0034345420 scopus 로고    scopus 로고
    • Nonmonotone spectral projected gradient methods on convex sets
    • Ernesto G. Birgin, José Mario Martinez, and Marcos Raydan. Nonmonotone spectral projected gradient methods on convex sets. SIAM Journal on Optimization, 10(4):1196-1211, 2000.
    • (2000) SIAM Journal on Optimization , vol.10 , Issue.4 , pp. 1196-1211
    • Birgin, E.G.1    Martinez, J.M.2    Raydan, M.3
  • 6
    • 26944441889 scopus 로고    scopus 로고
    • A study on SMO-type decomposition methods for support vector machines
    • Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
    • Pai-Hsuen Chen, Rong-En Fan, and Chih-Jen Lin. A study on SMO-type decomposition methods for support vector machines. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 2005.
    • (2005) Technical Report
    • Chen, P.-H.1    Fan, R.-E.2    Lin, C.-J.3
  • 8
    • 0000913324 scopus 로고    scopus 로고
    • SVMTorch: Support vector machines for large-scale regression problems
    • Ronan Collobert and Samy Bengio. SVMTorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research, 1:143-160,2001.
    • (2001) Journal of Machine Learning Research , vol.1 , pp. 143-160
    • Collobert, R.1    Bengio, S.2
  • 9
    • 0036583160 scopus 로고    scopus 로고
    • A parallel mixture of SVMs for very large scale problems
    • Ronan Collobert, Samy Bengio, and Yoshua Bengio. A parallel mixture of SVMs for very large scale problems. Neural Computation, 14(5):1105-1114, 2002.
    • (2002) Neural Computation , vol.14 , Issue.5 , pp. 1105-1114
    • Collobert, R.1    Bengio, S.2    Bengio, Y.3
  • 11
    • 33644511085 scopus 로고    scopus 로고
    • New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds
    • Yu-Hong Dai and Roger Fletcher. New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds. Mathematical Programming, 106(3):403-421, 2006.
    • (2006) Mathematical Programming , vol.106 , Issue.3 , pp. 403-421
    • Dai, Y.-H.1    Fletcher, R.2
  • 12
    • 15544385032 scopus 로고    scopus 로고
    • Projected Barzilai-Borwein methods for large-scale box-constrained quadratic programming
    • Yu-Hong Dai and Roger Fletcher. Projected Barzilai-Borwein methods for large-scale box-constrained quadratic programming. Numerische Mathematik, 100(1):21-47, 2005.
    • (2005) Numerische Mathematik , vol.100 , Issue.1 , pp. 21-47
    • Dai, Y.-H.1    Fletcher, R.2
  • 13
    • 8344226915 scopus 로고    scopus 로고
    • A fast parallel optimization for training support vector machine
    • P. Perner and A. Rosenfeld, editors. Springer Lecture Notes in Artificial Intelligence, Leipzig, Germany
    • Jian-Xiong Dong, Adam Krzyzak, and Ching Y. Suen. A fast parallel optimization for training support vector machine. In P. Perner and A. Rosenfeld, editors, Proceedings of 3rd International Conference on Machine Learning and Data Mining, volume 17, pages 96-105. Springer Lecture Notes in Artificial Intelligence, Leipzig, Germany, 2003.
    • (2003) Proceedings of 3rd International Conference on Machine Learning and Data Mining , vol.17 , pp. 96-105
    • Dong, J.-X.1    Krzyzak, A.2    Suen, C.Y.3
  • 14
    • 29144499905 scopus 로고    scopus 로고
    • Working set selection using second order information for training Support Vector Machines
    • Rong-En Fan, Pai-Hsuen Chen, and Chih-Jen Lin. Working set selection using second order information for training Support Vector Machines. Journal of Machine Learning Research, 6:1889-1918, 2005.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 1889-1918
    • Fan, R.-E.1    Chen, P.-H.2    Lin, C.-J.3
  • 16
    • 0036158552 scopus 로고    scopus 로고
    • A simple decomposition method for support vector machines
    • Chih-Wei Hsu and Chih-Jen Lin. A simple decomposition method for support vector machines. Machine Learning, 46:291-314, 2002.
    • (2002) Machine Learning , vol.46 , pp. 291-314
    • Hsu, C.-W.1    Lin, C.-J.2
  • 17
    • 0037399781 scopus 로고    scopus 로고
    • Polynomial-time decomposition algorithms for support vector machines
    • Don Hush and Clint Scovel. Polynomial-time decomposition algorithms for support vector machines. Machine Learning, 51:51-71, 2003.
    • (2003) Machine Learning , vol.51 , pp. 51-71
    • Hush, D.1    Scovel, C.2
  • 18
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • Bernard Schölkopf, C.J.C. Burges, and Alex Smola, editors. MIT Press, Cambridge, MA
    • Throstem Joachims. Making large-scale SVM learning practical. In Bernard Schölkopf, C.J.C. Burges, and Alex Smola, editors, Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, MA, 1998.
    • (1998) Advances in Kernel Methods - Support Vector Learning
    • Joachims, T.1
  • 19
    • 0036163654 scopus 로고    scopus 로고
    • Convergence of a generalized SMO algorithm for SVM classifier design
    • S. Sathiya Keerthi and Elmer G. Gilbert. Convergence of a generalized SMO algorithm for SVM classifier design. Machine Learning, 46:351-360, 2002.
    • (2002) Machine Learning , vol.46 , pp. 351-360
    • Keerthi, S.S.1    Gilbert, E.G.2
  • 20
    • 0035506741 scopus 로고    scopus 로고
    • On the convergence of the decomposition method for support vector machines
    • Chih-Jen Lin. On the convergence of the decomposition method for support vector machines. IEEE Transactions on Neural Networks, 12:1288-1298, 2001a.
    • (2001) IEEE Transactions on Neural Networks , vol.12 , pp. 1288-1298
    • Lin, C.-J.1
  • 21
    • 0038178786 scopus 로고    scopus 로고
    • Linear convergence of a decomposition method for support vector machines
    • Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
    • Chih-Jen Lin. Linear convergence of a decomposition method for support vector machines. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 2001b.
    • (2001) Technical Report
    • Lin, C.-J.1
  • 22
    • 0036129250 scopus 로고    scopus 로고
    • Asymptotic convergence of an SMO algorithm without any assumptions
    • Chih-Jen Lin. Asymptotic convergence of an SMO algorithm without any assumptions. IEEE Transactions on Neural Networks, 13:248-250, 2002.
    • (2002) IEEE Transactions on Neural Networks , vol.13 , pp. 248-250
    • Lin, C.-J.1
  • 23
    • 33745774600 scopus 로고
    • MPI: A message-passing interface standard (version 1.2)
    • Also available as Technical Report CS-94-230, Computer Science Dept., University of Tennesse, Knoxville, TN
    • Message Passing Interface Forum. MPI: A message-passing interface standard (version 1.2). International Journal of Super computing Applications, 8(3/4), 1995. URL http://www.mpi-forum.org. Also available as Technical Report CS-94-230, Computer Science Dept., University of Tennesse, Knoxville, TN.
    • (1995) International Journal of Super Computing Applications , vol.8 , Issue.3-4
  • 24
    • 0003446306 scopus 로고    scopus 로고
    • MINOS 5.5 user's guide
    • Department of Operation Research, Stanford University, Stanford CA
    • Bruce A. Murtagh and Michael A. Saunders. MINOS 5.5 user's guide. Technical report, Department of Operation Research, Stanford University, Stanford CA, 1998.
    • (1998) Technical Report
    • Murtagh, B.A.1    Saunders, M.A.2
  • 27
    • 0000859664 scopus 로고
    • An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds
    • Panos M. Pardalos and Naina Kovoor. An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds. Mathematical Programming, 46:321-328, 1990.
    • (1990) Mathematical Programming , vol.46 , pp. 321-328
    • Pardalos, P.M.1    Kovoor, N.2
  • 28
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • Bernard Schölkopf, C.J.C. Burges, and Alex Smola, editors. MIT Press, Cambridge, MA
    • John C. Platt. Fast training of support vector machines using sequential minimal optimization. In Bernard Schölkopf, C.J.C. Burges, and Alex Smola, editors, Advances in Kernel Methods -Support Vector Learning. MIT Press, Cambridge, MA, 1998.
    • (1998) Advances in Kernel Methods -Support Vector Learning
    • Platt, J.C.1
  • 29
    • 0034134885 scopus 로고    scopus 로고
    • A modified projection algorithm for large strictly convex quadratic programs
    • Valeria Ruggiero and Luca Zanni. A modified projection algorithm for large strictly convex quadratic programs. Journal of Optimization Theory and Applications, 104(2):281-299, 2000a.
    • (2000) Journal of Optimization Theory and Applications , vol.104 , Issue.2 , pp. 281-299
    • Ruggiero, V.1    Zanni, L.2
  • 30
    • 0012540092 scopus 로고    scopus 로고
    • Variable projection methods for large convex quadratic programs
    • Donato Trigiante, editor, Recent Trends in Numerical Analysis. Nova Science Publisher
    • Valeria Ruggiero and Luca Zanni. Variable projection methods for large convex quadratic programs. In Donato Trigiante, editor, Recent Trends in Numerical Analysis, volume 3 of Advances in the Theory of Computational Mathematics, pages 299-313. Nova Science Publisher, 2000b.
    • (2000) Advances in the Theory of Computational Mathematics , vol.3 , pp. 299-313
    • Ruggiero, V.1    Zanni, L.2
  • 31
    • 27744479938 scopus 로고    scopus 로고
    • On the working set selection in gradient projection-based decomposition techniques for support vector machines
    • Thomas Serafini and Luca Zanni. On the working set selection in gradient projection-based decomposition techniques for support vector machines. Optimization Methods and Software, 20:583-596, 2005.
    • (2005) Optimization Methods and Software , vol.20 , pp. 583-596
    • Serafini, T.1    Zanni, L.2
  • 32
    • 12444278992 scopus 로고    scopus 로고
    • Gradient projection methods for quadratic programs and applications in training support vector machines
    • Thomas Serafini, Gaetano Zanghirati, and Luca Zanni. Gradient projection methods for quadratic programs and applications in training support vector machines. Optimization Methods and Software, 20:353-378, 2005.
    • (2005) Optimization Methods and Software , vol.20 , pp. 353-378
    • Serafini, T.1    Zanghirati, G.2    Zanni, L.3
  • 33
  • 34
    • 21844440579 scopus 로고    scopus 로고
    • Core vector machines: Fast SVM training on very large data sets
    • Ivor W. Tsang, James T. Kwok, and Pak-Ming Cheung. Core vector machines: fast SVM training on very large data sets. Journal of Machine Learning Research, 6(4):363-392, 2005.
    • (2005) Journal of Machine Learning Research , vol.6 , Issue.4 , pp. 363-392
    • Tsang, I.W.1    Kwok, J.T.2    Cheung, P.-M.3
  • 36
    • 0037378238 scopus 로고    scopus 로고
    • A parallel solver for large quadratic programs in training support vector machines
    • Gaetano Zanghirati and Luca Zanni. A parallel solver for large quadratic programs in training support vector machines. Parallel Computing, 29:535-551, 2003.
    • (2003) Parallel Computing , vol.29 , pp. 535-551
    • Zanghirati, G.1    Zanni, L.2
  • 37
    • 33645025197 scopus 로고    scopus 로고
    • An improved gradient projection-based decomposition technique for support vector machines
    • Luca Zanni. An improved gradient projection-based decomposition technique for support vector machines. Computational Management Science, 3(2):131-145, 2006.
    • (2006) Computational Management Science , vol.3 , Issue.2 , pp. 131-145
    • Zanni, L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.