메뉴 건너뛰기




Volumn 15, Issue , 2011, Pages 909-917

Two-layer multiple kernel learning

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; CLASSIFICATION TASKS; GENERALIZATION PERFORMANCE; KERNEL MACHINE; MACHINE LEARNING PROBLEM; MULTILAYER STRUCTURES; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; MULTIPLE LAYERS; TWO LAYERS;

EID: 84862282715     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (100)

References (32)
  • 2
    • 26944453996 scopus 로고    scopus 로고
    • Learning convex combinations of continuously parameterized basic kernels
    • A. Argyriou, C. A. Micchelli, and M. Pontil. Learning convex combinations of continuously parameterized basic kernels. In COLT, pages 338-352, 2005.
    • (2005) COLT , pp. 338-352
    • Argyriou, A.1    Micchelli, C.A.2    Pontil, M.3
  • 3
    • 84858766876 scopus 로고    scopus 로고
    • Exploring large feature spaces with hierarchical multiple kernel learning
    • F. Bach. Exploring large feature spaces with hierarchical multiple kernel learning. In NIPS, pages 105-112, 2008.
    • (2008) NIPS , pp. 105-112
    • Bach, F.1
  • 4
    • 14344252374 scopus 로고    scopus 로고
    • Multiple kernel learning, conic duality, and the SMO algorithm
    • F. Bach, G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In ICML, 2004.
    • (2004) ICML
    • Bach, F.1    Lanckriet, G.2    Jordan, M.I.3
  • 6
    • 78149327741 scopus 로고    scopus 로고
    • Kernel methods for deep learning
    • Y. Cho and L. K. Saul. Kernel methods for deep learning. In NIPS, pages 342-350, 2009.
    • (2009) NIPS , pp. 342-350
    • Cho, Y.1    Saul, L.K.2
  • 7
    • 78149334888 scopus 로고    scopus 로고
    • Large-margin classification in infinite neural networks
    • Y. Cho and L. K. Saul. Large-margin classification in infinite neural networks. Neural Computation, 22(10):2678-2697, 2010.
    • (2010) Neural Computation , vol.22 , Issue.10 , pp. 2678-2697
    • Cho, Y.1    Saul, L.K.2
  • 8
    • 84858743760 scopus 로고    scopus 로고
    • Learning non-linear combinations of kernels
    • C. Cortes, M. Mohri, and A. Rostamizadeh. Learning non-linear combinations of kernels. In NIPS, 2009.
    • (2009) NIPS
    • Cortes, C.1    Mohri, M.2    Rostamizadeh, A.3
  • 9
    • 34249753618 scopus 로고
    • Support-vector networks
    • C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, 1995.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 12
    • 56449124689 scopus 로고    scopus 로고
    • Localized multiple kernel learning
    • M. Gönen and E. Alpaydin. Localized multiple kernel learning. In ICML, pages 352-359, 2008.
    • (2008) ICML , pp. 352-359
    • Gönen, M.1    Alpaydin, E.2
  • 13
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006.
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.W.3
  • 14
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • G. E. Hinton and R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507, 2006.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.2
  • 19
    • 34547967782 scopus 로고    scopus 로고
    • An empirical evaluation of deep architectures on problems with many factors of variation
    • H. Larochelle, D. Erhan, A. C. Courville, J. Bergstra, and Y. Bengio. An empirical evaluation of deep architectures on problems with many factors of variation. In ICML, pages 473-480, 2007.
    • (2007) ICML , pp. 473-480
    • Larochelle, H.1    Erhan, D.2    Courville, A.C.3    Bergstra, J.4    Bengio, Y.5
  • 21
    • 34547971778 scopus 로고    scopus 로고
    • More efficiency in multiple kernel learning
    • A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. More efficiency in multiple kernel learning. In ICML, pages 775-782, 2007.
    • (2007) ICML , pp. 775-782
    • Rakotomamonjy, A.1    Bach, F.2    Canu, S.3    Grandvalet, Y.4
  • 24
    • 84864066318 scopus 로고    scopus 로고
    • A general and efficient multiple kernel learning algorithm
    • S. Sonnenburg, G. Rätsch, and C. Schäfer. A general and efficient multiple kernel learning algorithm. In NIPS, 2005.
    • (2005) NIPS
    • Sonnenburg, S.1    Rätsch, G.2    Schäfer, C.3
  • 26
    • 33746031418 scopus 로고    scopus 로고
    • Learning bounds for support vector machines with learned kernels
    • N. Srebro and S. Ben-David. Learning bounds for support vector machines with learned kernels. In COLT, pages 169-183, 2006.
    • (2006) COLT , pp. 169-183
    • Srebro, N.1    Ben-David, S.2
  • 28
    • 71149100224 scopus 로고    scopus 로고
    • More generality in efficient multiple kernel learning
    • M. Varma and B. R. Babu. More generality in efficient multiple kernel learning. In ICML, page 134, 2009.
    • (2009) ICML , pp. 134
    • Varma, M.1    Babu, B.R.2
  • 29
    • 84863385308 scopus 로고    scopus 로고
    • An extended level method for efficient multiple kernel learning
    • Z. Xu, R. Jin, I. King, and M. R. Lyu. An extended level method for efficient multiple kernel learning. In NIPS, pages 1825-1832, 2008.
    • (2008) NIPS , pp. 1825-1832
    • Xu, Z.1    Jin, R.2    King, I.3    Lyu, M.R.4
  • 30
    • 84898069211 scopus 로고    scopus 로고
    • Generalization bounds for learning the kernel
    • Y. Ying and C. Campbell. Generalization bounds for learning the kernel. In COLT, 2009.
    • (2009) COLT
    • Ying, Y.1    Campbell, C.2
  • 32
    • 0347585601 scopus 로고    scopus 로고
    • Kernel logistic regression and the import vector machine
    • J. Zhu and T. Hastie. Kernel logistic regression and the import vector machine. In NIPS, pages 1081-1088, 2001.
    • (2001) NIPS , pp. 1081-1088
    • Zhu, J.1    Hastie, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.