메뉴 건너뛰기




Volumn 41, Issue 6, 2008, Pages 2107-2119

Optimizing the data-dependent kernel under a unified kernel optimization framework

Author keywords

Fisher criteria; Kernel induced feature space; Kernel machine; Kernel optimization

Indexed keywords

CLUSTER ANALYSIS; COVARIANCE MATRIX; FEATURE EXTRACTION; OPTIMIZATION; PROBLEM SOLVING;

EID: 38849204864     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2007.10.006     Document Type: Article
Times cited : (47)

References (40)
  • 4
    • 0036161011 scopus 로고    scopus 로고
    • Choosing multiple parameters for support vector machines
    • Chapelle O., Vapnik V., Bousquet O., and Mukherjee S. Choosing multiple parameters for support vector machines. Mach. Learn. 46 1 (2002) 131-159
    • (2002) Mach. Learn. , vol.46 , Issue.1 , pp. 131-159
    • Chapelle, O.1    Vapnik, V.2    Bousquet, O.3    Mukherjee, S.4
  • 5
    • 0034271876 scopus 로고    scopus 로고
    • The evidence framework applied to support vector machines
    • Kwok J.T. The evidence framework applied to support vector machines. IEEE Trans. Neural Networks 11 5 (2000) 1162-1173
    • (2000) IEEE Trans. Neural Networks , vol.11 , Issue.5 , pp. 1162-1173
    • Kwok, J.T.1
  • 6
    • 0036163572 scopus 로고    scopus 로고
    • Bayesian methods for support vector machines: evidence and predictive class probabilities
    • Sollich P. Bayesian methods for support vector machines: evidence and predictive class probabilities. Mach. Learn. 46 1-3 (2002) 21-52
    • (2002) Mach. Learn. , vol.46 , Issue.1-3 , pp. 21-52
    • Sollich, P.1
  • 7
    • 2542639357 scopus 로고    scopus 로고
    • An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels
    • Lee M.M.S., Keerthi S.S., Ong C.J., and DeCoste D. An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels. IEEE Trans. Neural Networks 15 3 (2004) 750-757
    • (2004) IEEE Trans. Neural Networks , vol.15 , Issue.3 , pp. 750-757
    • Lee, M.M.S.1    Keerthi, S.S.2    Ong, C.J.3    DeCoste, D.4
  • 8
    • 27344455216 scopus 로고    scopus 로고
    • Learning the kernel parameters in kernel minimum distance classifier
    • Zhang D., Chen S., and Zhou Z.-H. Learning the kernel parameters in kernel minimum distance classifier. Pattern Recognition 39 1 (2006) 133-135
    • (2006) Pattern Recognition , vol.39 , Issue.1 , pp. 133-135
    • Zhang, D.1    Chen, S.2    Zhou, Z.-H.3
  • 16
    • 0032786569 scopus 로고    scopus 로고
    • Improving support vector machine classifiers by modifying kernel functions
    • Amari S., and Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12 6 (1999) 783-789
    • (1999) Neural Networks , vol.12 , Issue.6 , pp. 783-789
    • Amari, S.1    Wu, S.2
  • 22
    • 35448973729 scopus 로고    scopus 로고
    • A kernel optimization method based on the localized kernel Fisher criterion
    • Chen B., Liu H., and Bao Z. A kernel optimization method based on the localized kernel Fisher criterion. Pattern Recognition 41 3 (2008) 1098-1109
    • (2008) Pattern Recognition , vol.41 , Issue.3 , pp. 1098-1109
    • Chen, B.1    Liu, H.2    Bao, Z.3
  • 23
    • 33746131974 scopus 로고    scopus 로고
    • Kernel-based distance metric learning for microarray data classification
    • Xiong H.L., and Chen X.-w. Kernel-based distance metric learning for microarray data classification. BMC Bioinformatics 7 (2006) 299
    • (2006) BMC Bioinformatics , vol.7 , pp. 299
    • Xiong, H.L.1    Chen, X.-w.2
  • 24
    • 38849084561 scopus 로고    scopus 로고
    • C. Blake, E. Keogh, C.J. Merz, UCI Repository of Machine Learning Databases, Department of Information and Computer Science, University of California, Irvine, CA, 1998 [Online], available from〈http://www.ics.uci.edu/mlearn〉.
    • C. Blake, E. Keogh, C.J. Merz, UCI Repository of Machine Learning Databases, Department of Information and Computer Science, University of California, Irvine, CA, 1998 [Online], available from〈http://www.ics.uci.edu/mlearn〉.
  • 25
    • 38849180033 scopus 로고    scopus 로고
    • D. Michie, D.J. Spiegelhalter, C.C. Taylor (Eds.), Machine Learning, Neural and Statistical Classification, Ellis Horwood, 1994, data sets available from 〈http://www.liacc.up.pt/ML/statlog/datasets.html〉.
    • D. Michie, D.J. Spiegelhalter, C.C. Taylor (Eds.), Machine Learning, Neural and Statistical Classification, Ellis Horwood, 1994, data sets available from 〈http://www.liacc.up.pt/ML/statlog/datasets.html〉.
  • 26
    • 38849189315 scopus 로고    scopus 로고
    • S. Roweis, Finding the first few eigenvectors in a large space, Individual Research Note, available from 〈http://www.cs.toronto.edu/∼roweis/〉 .
    • S. Roweis, Finding the first few eigenvectors in a large space, Individual Research Note, available from 〈http://www.cs.toronto.edu/∼roweis/〉 .
  • 28
    • 23844557175 scopus 로고    scopus 로고
    • Radar HRRP target recognition based on higher order spectra
    • Du L., Liu H., Bao Z., and Xing M. Radar HRRP target recognition based on higher order spectra. IEEE Trans. Signal Process. 53 7 (2005) 2359-2368
    • (2005) IEEE Trans. Signal Process. , vol.53 , Issue.7 , pp. 2359-2368
    • Du, L.1    Liu, H.2    Bao, Z.3    Xing, M.4
  • 29
    • 33744538338 scopus 로고    scopus 로고
    • A two-distribution compounded statistical model for radar HRRP target recognition
    • Du L., Liu H., Bao Z., and Zhang J. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Trans. Signal Process. 54 6 (2006) 2226-2238
    • (2006) IEEE Trans. Signal Process. , vol.54 , Issue.6 , pp. 2226-2238
    • Du, L.1    Liu, H.2    Bao, Z.3    Zhang, J.4
  • 30
    • 0036833268 scopus 로고    scopus 로고
    • Bispectrum based approach to high radar range profile for automatic target recognition
    • Pei B., and Bao Z. Bispectrum based approach to high radar range profile for automatic target recognition. Pattern Recognition 35 11 (2002) 2643-2651
    • (2002) Pattern Recognition , vol.35 , Issue.11 , pp. 2643-2651
    • Pei, B.1    Bao, Z.2
  • 31
    • 0036465092 scopus 로고    scopus 로고
    • Properties of high-resolution range profiles
    • Mengdao X., Zheng B., and Pei B. Properties of high-resolution range profiles. Opt. Eng. 41 2 (2002) 493-504
    • (2002) Opt. Eng. , vol.41 , Issue.2 , pp. 493-504
    • Mengdao, X.1    Zheng, B.2    Pei, B.3
  • 32
    • 24944467419 scopus 로고    scopus 로고
    • H. Liu, Z. Bao, Radar HRR profiles recognition based on SVM with power-transformed-correlation kernel, Lecture Notes in Computer Science, vol. 3174, no. (I), 2004, pp. 531-536.
    • H. Liu, Z. Bao, Radar HRR profiles recognition based on SVM with power-transformed-correlation kernel, Lecture Notes in Computer Science, vol. 3174, no. (I), 2004, pp. 531-536.
  • 33
    • 0031061906 scopus 로고    scopus 로고
    • The Box-Cox metric for nearest neighbor classification improvement
    • Heiden R., and Groen F.C.A. The Box-Cox metric for nearest neighbor classification improvement. Pattern Recognition 30 (1997) 273-279
    • (1997) Pattern Recognition , vol.30 , pp. 273-279
    • Heiden, R.1    Groen, F.C.A.2
  • 34
    • 3042673775 scopus 로고    scopus 로고
    • Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion
    • Loog M., and Duin R.P.W. Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion. IEEE Trans. Pattern Anal. Mach. Intelli. 26 6 (2004) 732-739
    • (2004) IEEE Trans. Pattern Anal. Mach. Intelli. , vol.26 , Issue.6 , pp. 732-739
    • Loog, M.1    Duin, R.P.W.2
  • 35
    • 33745820770 scopus 로고    scopus 로고
    • G. Dai, D.-Y. Yeung, H. Chang, Extending kernel Fisher discriminant analysis with the weighted pairwise Chernoff criterion, in: Proceedings of ECCV 2006, Springer, Berlin, Heidelberg, 2006, pp. 308-320.
    • G. Dai, D.-Y. Yeung, H. Chang, Extending kernel Fisher discriminant analysis with the weighted pairwise Chernoff criterion, in: Proceedings of ECCV 2006, Springer, Berlin, Heidelberg, 2006, pp. 308-320.
  • 38
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • Tenenbaum J., Silva V., and Langford J. A global geometric framework for nonlinear dimensionality reduction. Science 290 22 (2000) 2319-2323
    • (2000) Science , vol.290 , Issue.22 , pp. 2319-2323
    • Tenenbaum, J.1    Silva, V.2    Langford, J.3
  • 39
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis S., and Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 290 22 (2000) 2323-2326
    • (2000) Science , vol.290 , Issue.22 , pp. 2323-2326
    • Roweis, S.1    Saul, L.2
  • 40
    • 33947492041 scopus 로고    scopus 로고
    • Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics
    • Yang J., Zhang D., Yang J.-y., and Niu B. Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 29 4 (2007) 650-664
    • (2007) IEEE Trans. Pattern Anal. Mach. Intell. , vol.29 , Issue.4 , pp. 650-664
    • Yang, J.1    Zhang, D.2    Yang, J.-y.3    Niu, B.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.