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Volumn , Issue , 2005, Pages 349-356

Very large SVM training using Core Vector Machines

Author keywords

[No Author keywords available]

Indexed keywords

CORE SET; DATA SETS; GAUSSIAN KERNELS; KERNEL METHODS; MINIMUM ENCLOSING BALL; OPTIMAL SOLUTIONS; REAL WORLD DATA; SCALE-UP; SPACE COMPLEXITY; TIME COMPLEXITY; TRAINING SETS; VECTOR MACHINES;

EID: 33947417485     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (30)

References (20)
  • 4
    • 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
  • 5
    • 0036583160 scopus 로고    scopus 로고
    • A parallel mixture of SVMs for very large scale problems
    • Collobert, R., Bengio, S., & Bengio, Y. (2002). A parallel mixture of SVMs for very large scale problems. Neural Computation, 14, 1105-1114.
    • (2002) Neural Computation , vol.14 , pp. 1105-1114
    • Collobert, R.1    Bengio, S.2    Bengio, Y.3
  • 6
    • 0041494125 scopus 로고    scopus 로고
    • Efficient SVM training using low-rank kernel representation
    • Fine, S., & Scheinberg, K. (2001). Efficient SVM training using low-rank kernel representation. Journal of Machine Learning Research, 2, 243-264.
    • (2001) Journal of Machine Learning Research , vol.2 , pp. 243-264
    • Fine, S.1    Scheinberg, K.2
  • 8
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale support vector machine learning practical
    • B. Schölkopf, C. Burges and A. Smola (Eds.) Cambridge, MA: MIT Press
    • Joachims, T. (1999). Making large-scale support vector machine learning practical. In B. Schölkopf, C. Burges and A. Smola (Eds.), Advances in kernel methods - Support vector learning, 169-184. Cambridge, MA: MIT Press.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 169-184
    • Joachims, T.1
  • 14
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • B. Schölkopf, C. Burges and A. Smola (Eds.) Cambridge, MA: MIT Press
    • Platt, J. (1999). Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. Burges and A. Smola (Eds.), Advances in kernel methods - support vector learning, 185-208. Cambridge, MA: MIT Press.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 185-208
    • Platt, J.1
  • 17
    • 4043137356 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • Smola, A., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199-222.
    • (2004) Statistics and Computing , vol.14 , pp. 199-222
    • Smola, A.1    Schölkopf, B.2
  • 18
    • 0033220728 scopus 로고    scopus 로고
    • Support vector domain description
    • Tax, D., & Duin, R. (1999). Support vector domain description. Pattern Recognition Letters, 20, 1191-1199.
    • (1999) Pattern Recognition Letters , vol.20 , pp. 1191-1199
    • Tax, D.1    Duin, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.