메뉴 건너뛰기




Volumn 13, Issue , 2012, Pages 607-642

Non-sparse multiple kernel fisher discriminant analysis

Author keywords

Kernel fisher discriminant analysis; Multiple kernel learning; Regularised least squares; Support vector machines

Indexed keywords

APPLICATION AREA; DATA SETS; KERNEL FISHER DISCRIMINANT ANALYSIS; KERNEL MACHINE; KERNEL WEIGHT; LEAST SQUARE; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; OPTIMISATIONS; REGULARISATION; SEMI-INFINITE PROGRAMS; SUPPORT VECTOR MACHINE (SVM); UNIFIED FRAMEWORK;

EID: 84859457072     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (43)

References (73)
  • 2
    • 0034296402 scopus 로고    scopus 로고
    • Generalized discriminant analysis using a kernel approach
    • G. Baudat and F. Anouar. Generalized discriminant analysis using a kernel approach. Neural Computation, 12:2385-2404, 2000.
    • (2000) Neural Computation , vol.12 , pp. 2385-2404
    • Baudat, G.1    Anouar, F.2
  • 7
    • 0036161011 scopus 로고    scopus 로고
    • Choosing multiple parameters for support vector machines
    • DOI 10.1023/A:1012450327387
    • O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46:131-159, 2002. (Pubitemid 34129966)
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 131-159
    • Chapelle, O.1    Vapnik, V.2    Bousquet, O.3    Mukherjee, S.4
  • 16
    • 0000764772 scopus 로고
    • The use of multiple measurements in taxonomic problems
    • R. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179-188, 1936.
    • (1936) Annals of Eugenics , vol.7 , pp. 179-188
    • Fisher, R.1
  • 20
    • 0036582564 scopus 로고    scopus 로고
    • Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel fisher discriminant analysis
    • T. Gestel, J. Suykens, G. Lanckriet, A. Lambrechts, B. Moor, and J. Vandewalle. Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel fisher discriminant analysis. Machine Learning, 14(5):1115-1147, 2002.
    • (2002) Machine Learning , vol.14 , Issue.5 , pp. 1115-1147
    • Gestel, T.1    Suykens, J.2    Lanckriet, G.3    Lambrechts, A.4    Moor, B.5    Vandewalle, J.6
  • 21
    • 0001219859 scopus 로고
    • Regularization theory and neural networks architectures
    • F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures. Neural Computation, 7:219-269, 1995.
    • (1995) Neural Computation , vol.7 , pp. 219-269
    • Girosi, F.1    Jones, M.2    Poggio, T.3
  • 22
  • 23
    • 34247576789 scopus 로고    scopus 로고
    • The pyramid match kernel: Efficient learning with sets of features
    • K. Grauman and T. Darrell. The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research, 8:725-760, 2007. (Pubitemid 46677049)
    • (2007) Journal of Machine Learning Research , vol.8 , pp. 725-760
    • Grauman, K.1    Darrell, T.2
  • 26
    • 0027657329 scopus 로고
    • Semi-infinite programming: Theory, methods, and applications
    • R. Hettich and K. Kortanek. Semi-infinite programming: Theory, methods, and applications. SIAM Review, 35(3):380-429, 1993. (Pubitemid 23712350)
    • (1993) SIAM Review , vol.35 , Issue.3 , pp. 380-429
    • Hettich, R.1    Kortanek, K.O.2
  • 28
    • 0037313407 scopus 로고    scopus 로고
    • SMO algorithm for least-squares SVM formulations
    • DOI 10.1162/089976603762553013
    • S. Keerthi and S. Shevade. Smo algorithm for least squares svm formulations. Neural Computation, 15(2):487-507, 2003. (Pubitemid 37049831)
    • (2003) Neural Computation , vol.15 , Issue.2 , pp. 487-507
    • Keerthi, S.S.1    Shevade, S.K.2
  • 36
    • 33646887390 scopus 로고
    • On the limited memory BFGS method for large scale optimization
    • D. Liu and J. Nocedal. On the limited memory method for large scale optimization. Mathematical Programming B, 45(3):503-528, 1989. (Pubitemid 20660315)
    • (1989) Mathematical Programming, Series B , vol.45 , Issue.3 , pp. 503-528
    • Liu Dong, C.1    Nocedal Jorge2
  • 37
    • 79751524883 scopus 로고    scopus 로고
    • First and second order smo algorithms for ls-svm classifiers
    • J. Lopez and J. Suykens. First and second order smo algorithms for ls-svm classifiers. Neural Processing Letters, 33(1):31-44, 2011.
    • (2011) Neural Processing Letters , vol.33 , Issue.1 , pp. 31-44
    • Lopez, J.1    Suykens, J.2
  • 38
    • 3042535216 scopus 로고    scopus 로고
    • Distincetive image features from scale-invariant keypoints
    • D. Lowe. Distincetive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
    • (2004) International Journal of Computer Vision , vol.60 , Issue.2 , pp. 91-110
    • Lowe, D.1
  • 39
    • 1542337814 scopus 로고    scopus 로고
    • PhD Thesis University of Technology, Berlin, Germany
    • S. Mika. Kernel fisher discriminants. PhD Thesis, University of Technology, Berlin, Germany, 2002.
    • (2002) Kernel Fisher Discriminants
    • Mika, S.1
  • 45
    • 78049485759 scopus 로고    scopus 로고
    • An automated combination of kernels for predicting protein subcellular localization
    • C. Ong and A. Zien. An automated combination of kernels for predicting protein subcellular localization. In Workshop on Algorithms in Bioinformatics, 2008.
    • (2008) Workshop on Algorithms in Bioinformatics
    • Ong, C.1    Zien, A.2
  • 53
    • 0002536264 scopus 로고    scopus 로고
    • Playing billiard in version space
    • P. Rujan. Playing billiard in version space. Neural Computation, 9:99-122, 1997.
    • (1997) Neural Computation , vol.9 , pp. 99-122
    • Rujan, P.1
  • 62
    • 0032638628 scopus 로고    scopus 로고
    • Least squares support vector machine classifiers
    • J. Suykens and J. Vandewalle. Least squares support vector machine classifiers. Neural Processing Letters, 9:293-300, 1999.
    • (1999) Neural Processing Letters , vol.9 , pp. 293-300
    • Suykens, J.1    Vandewalle, J.2
  • 71
    • 44649123652 scopus 로고    scopus 로고
    • Multi-class discriminant kernel learning via convex programming
    • J. Ye, S. Ji, and J. Chen. Multi-class discriminant kernel learning via convex programming. Journal of Machine Learning Research, 9:719-758, 2008.
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 719-758
    • Ye, J.1    Ji, S.2    Chen, J.3
  • 72
    • 33846580425 scopus 로고    scopus 로고
    • Local features and kernels for classification of texture and object categories: A comprehensive study
    • DOI 10.1007/s11263-006-9794-4
    • J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision, 73(2):213-238, 2007. (Pubitemid 46181625)
    • (2007) International Journal of Computer Vision , vol.73 , Issue.2 , pp. 213-238
    • Zhang, J.1    Marszalek, M.2    Lazebnik, S.3    Schmid, C.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.