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Volumn , Issue , 2004, Pages 41-48

Multiple kernel learning, conic duality, and the SMO algorithm

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER PROGRAMMING; IONOSPHERE; OPTIMIZATION; PROBLEM SOLVING; SET THEORY; TUMORS;

EID: 14344252374     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (1067)

References (13)
  • 1
    • 0001614864 scopus 로고    scopus 로고
    • The MOSEK interior point optimizer for linear programming: An implementation of the homogeneous algorithm
    • Andersen, E. D., & Andersen, K. D. (2000). The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm. High Perf. Optimization (pp. 197-232).
    • (2000) High Perf. Optimization , pp. 197-232
    • Andersen, E.D.1    Andersen, K.D.2
  • 3
    • 0002709342 scopus 로고    scopus 로고
    • Feature selection via concave minimization and support vector machines
    • San Mateo, CA: Morgan Kaufmann
    • Bradley, P. S., & Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machines. International Conference on Machine Learning. San Mateo, CA: Morgan Kaufmann.
    • (1998) International Conference on Machine Learning
    • Bradley, P.S.1    Mangasarian, O.L.2
  • 5
    • 0036161011 scopus 로고    scopus 로고
    • Choosing multiple parameters for support vector machines
    • Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46, 131-159.
    • (2002) Machine Learning , vol.46 , pp. 131-159
    • Chapelle, O.1    Vapnik, V.2    Bousquet, O.3    Mukherjee, S.4
  • 6
    • 84899001870 scopus 로고    scopus 로고
    • Adaptive scaling for feature selection in SVMs
    • Cambridge, MA: MIT Press
    • Grandvalet, Y., & Canu, S. (2003). Adaptive scaling for feature selection in SVMs. Neural Information Processing Systems. Cambridge, MA: MIT Press.
    • (2003) Neural Information Processing Systems
    • Grandvalet, Y.1    Canu, S.2
  • 7
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale support vector machine learning practical
    • Cambridge, MA: MIT Press
    • Joachims, T. (1998). Making large-scale support vector machine learning practical. Advances in Kernel Methods: Support Vector Machines. Cambridge, MA: MIT Press.
    • (1998) Advances in Kernel Methods: Support Vector Machines
    • Joachims, T.1
  • 10
    • 0040165092 scopus 로고    scopus 로고
    • Practical aspects of the moreau-yosida regularization: Theoretical preliminaries
    • Lemarechal, C., & Sagastizabal, C. (1997). Practical aspects of the Moreau-Yosida regularization: Theoretical preliminaries. SIAM J. Optim., 7, 867-895.
    • (1997) SIAM J. Optim. , vol.7 , pp. 867-895
    • Lemarechal, C.1    Sagastizabal, C.2
  • 13
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • Cambridge, MA: MIT Press
    • Platt, J. (1998). Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press.
    • (1998) Advances in Kernel Methods: Support Vector Learning
    • Platt, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.