메뉴 건너뛰기




Volumn 2, Issue , 2012, Pages 1421-1429

Q-MKL: Matrix-induced regularization in Multi-Kernel Learning with applications to neuroimaging

Author keywords

[No Author keywords available]

Indexed keywords

ALZHEIMER'S DISEASE; COVARIANCE STRUCTURES; MIXING COEFFICIENT; MULTI-KERNEL LEARNING; MULTIPLE KERNEL LEARNING; OPTIMAL COMBINATION; QUADRATIC FUNCTION; RADEMACHER COMPLEXITY;

EID: 84877788381     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (21)

References (30)
  • 1
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • I. Guyon and A. Elisseeff. An introduction to variable and feature selection. JMLR, 3:1157-1182, 2003.
    • (2003) JMLR , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 2
    • 70450161376 scopus 로고    scopus 로고
    • Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers
    • P. V. Gehler and S. Nowozin. Let the kernel figure it out; principled learning of pre-processing for kernel classifiers. CVPR, 2009.
    • (2009) CVPR
    • Gehler, P.V.1    Nowozin, S.2
  • 3
    • 79551576499 scopus 로고    scopus 로고
    • Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population
    • C. Hinrichs, V. Singh, G. Xu, and S.C. Johnson. Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population. Neuroimage, 55(2):574-589, 2011.
    • (2011) Neuroimage , vol.55 , Issue.2 , pp. 574-589
    • Hinrichs, C.1    Singh, V.2    Xu, G.3    Johnson, S.C.4
  • 4
    • 79952073234 scopus 로고    scopus 로고
    • Multimodal classification of Alzheimer's disease and mild cognitive impairment
    • D. Zhang, Y. Wang, L. Zhou, H. Yuan, and D. Shen. Multimodal Classification of Alzheimer's Disease and Mild Cognitive Impairment. NeuroImage, 55(3):856-867, 2011.
    • (2011) NeuroImage , vol.55 , Issue.3 , pp. 856-867
    • Zhang, D.1    Wang, Y.2    Zhou, L.3    Yuan, H.4    Shen, D.5
  • 8
    • 85067032737 scopus 로고    scopus 로고
    • On feature combination for multiclass object classification
    • P. V. Gehler and S. Nowozin. On feature combination for multiclass object classification. In ICCV, 2009.
    • (2009) ICCV
    • Gehler, P.V.1    Nowozin, S.2
  • 9
    • 77952494909 scopus 로고    scopus 로고
    • Group-sensitive multiple kernel learning for object categorization
    • J. Yang, Y. Li, Y. Tian, L. Duan, and W. Gao. Group-sensitive multiple kernel learning for object categorization. In ICCV, 2009.
    • (2009) ICCV
    • Yang, J.1    Li, Y.2    Tian, Y.3    Duan, L.4    Gao, W.5
  • 10
    • 37849014569 scopus 로고    scopus 로고
    • Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies
    • P. Vemuri, J.L. Gunter, M. L. Senjem, J. L. Whitwell, K. Kantarci, D. S. Knopman, et al. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage, 39(3):1186-1197, 2008.
    • (2008) Neuroimage , vol.39 , Issue.3 , pp. 1186-1197
    • Vemuri, P.1    Gunter, J.L.2    Senjem, M.L.3    Whitwell, J.L.4    Kantarci, K.5    Knopman, D.S.6
  • 12
    • 14344252374 scopus 로고    scopus 로고
    • Multiple kernel learning, conic duality, and the SMO algorithm
    • F. R. Bach, G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In ICML, 2004.
    • (2004) ICML
    • Bach, F.R.1    Lanckriet, G.2    Jordan, M.I.3
  • 13
    • 77955993905 scopus 로고    scopus 로고
    • Online-batch strongly convex multi Kernel learning
    • F. Orabona, L. Jie, and B. Caputo. Online-Batch Strongly Convex Multi Kernel Learning. In CVPR, 2010.
    • (2010) CVPR
    • Orabona, F.1    Jie, L.2    Caputo, B.3
  • 15
    • 21844468979 scopus 로고    scopus 로고
    • Learning the kernel with hyperkernels
    • C.S. Ong, A. Smola, and B. Williamson. Learning the kernel with hyperkernels. JMLR, 6:1045-1071, 2005.
    • (2005) JMLR , vol.6 , pp. 1045-1071
    • Ong, C.S.1    Smola, A.2    Williamson, B.3
  • 16
    • 77956000518 scopus 로고    scopus 로고
    • Learning Kernels for variants of normalized cuts: Convex relaxations and applications
    • L. Mukherjee, V. Singh, J. Peng, and C. Hinrichs. Learning Kernels for variants of Normalized Cuts: Convex Relaxations and Applications. CVPR, 2010.
    • (2010) CVPR
    • Mukherjee, L.1    Singh, V.2    Peng, J.3    Hinrichs, C.4
  • 18
    • 85027388762 scopus 로고    scopus 로고
    • Exploring large feature spaces with hierarchical multiple kernel learning
    • F. R. Bach. Exploring large feature spaces with hierarchical multiple kernel learning. In NIPS, 2008.
    • (2008) NIPS
    • Bach, F.R.1
  • 21
    • 77956550918 scopus 로고    scopus 로고
    • Generalization bounds for learning kernels
    • C. Cortes, M. Mohri, and A. Rostamizadeh. Generalization bounds for learning kernels. In ICML, 2010.
    • (2010) ICML
    • Cortes, C.1    Mohri, M.2    Rostamizadeh, A.3
  • 22
    • 77949873915 scopus 로고    scopus 로고
    • Kernel entropy component analysis
    • R. Jenssen. Kernel entropy component analysis. IEEE Trans. PAMI, pages 847-860, 2009.
    • (2009) IEEE Trans. PAMI , pp. 847-860
    • Jenssen, R.1
  • 23
    • 0012993529 scopus 로고    scopus 로고
    • Orthogonal series density estimation and the kernel eigenvalue problem
    • M. Girolami. Orthogonal series density estimation and the kernel eigenvalue problem. Neural Computation, 14(3):669-688, 2002.
    • (2002) Neural Computation , vol.14 , Issue.3 , pp. 669-688
    • Girolami, M.1
  • 24
    • 0036737108 scopus 로고    scopus 로고
    • Generalized information potential criterion for adaptive system training
    • D. Erdogmus and J.C. Principe. Generalized information potential criterion for adaptive system training. IEEE Trans. Neural Networks, 13(5):1035-1044, 2002.
    • (2002) IEEE Trans. Neural Networks , vol.13 , Issue.5 , pp. 1035-1044
    • Erdogmus, D.1    Principe, J.C.2
  • 25
    • 71149091247 scopus 로고    scopus 로고
    • Multiple indefinite kernel learning with mixed norm regularization
    • M. Kowalski, M. Szafranski, and L. Ralaivola. Multiple indefinite kernel learning with mixed norm regularization. In ICML, 2009.
    • (2009) ICML
    • Kowalski, M.1    Szafranski, M.2    Ralaivola, L.3
  • 26
    • 84862273336 scopus 로고    scopus 로고
    • Improved natural language learning via variance-regularization support vector machines
    • S. Bergsma, D. Lin, and D. Schuurmans. Improved Natural Language Learning via Variance-Regularization Support Vector Machines. In CoNLL, 2010.
    • (2010) CoNLL
    • Bergsma, S.1    Lin, D.2    Schuurmans, D.3
  • 27
    • 85161983244 scopus 로고    scopus 로고
    • Spatial and anatomical regularization of SVM for brain image analysis
    • R. Cuingnet, M. Chupin, H. Benali, and O. Colliot. Spatial and anatomical regularization of SVM for brain image analysis. In NIPS, 2010.
    • (2010) NIPS
    • Cuingnet, R.1    Chupin, M.2    Benali, H.3    Colliot, O.4
  • 28
    • 77949509297 scopus 로고    scopus 로고
    • Maximum relative margin and data-dependent regularization
    • P. Shivaswamy and T. Jebara. Maximum relative margin and data-dependent regularization. JMLR, 11:747-788, 2010.
    • (2010) JMLR , vol.11 , pp. 747-788
    • Shivaswamy, P.1    Jebara, T.2
  • 29
    • 85161982879 scopus 로고    scopus 로고
    • Learning kernels with radiuses of minimum enclosing balls
    • K. Gai, G. Chen, and C. Zhang. Learning kernels with radiuses of minimum enclosing balls. In NIPS, 2010.
    • (2010) NIPS
    • Gai, K.1    Chen, G.2    Zhang, C.3
  • 30
    • 33144484244 scopus 로고    scopus 로고
    • Ways toward an early diagnosis in Alzheimers disease: The Alzheimer's disease neuroimaging initiative
    • S. G. Mueller, M. W. Weiner, et al. Ways toward an early diagnosis in Alzheimers disease: The Alzheimer's Disease Neuroimaging Initiative. J. of the Alzheimer's Association, 1(1):55-66, 2005.
    • (2005) J. of the Alzheimer's Association , vol.1 , Issue.1 , pp. 55-66
    • Mueller, S.G.1    Weiner, M.W.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.