메뉴 건너뛰기




Volumn 42, Issue 1, 2009, Pages 93-104

Discriminatively regularized least-squares classification

Author keywords

Classifier design; Discriminative information; Manifold learning; Pattern recognition

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL FLUID DYNAMICS; COMPUTER NETWORKS; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; SUPPORT VECTOR MACHINES; VECTORS;

EID: 51649109792     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.07.010     Document Type: Article
Times cited : (112)

References (62)
  • 2
    • 0040675320 scopus 로고    scopus 로고
    • On different facets of regularization theory
    • Chen Z., and Haykin S. On different facets of regularization theory. Neural Comput. 14 12 (2002) 2791-2846
    • (2002) Neural Comput. , vol.14 , Issue.12 , pp. 2791-2846
    • Chen, Z.1    Haykin, S.2
  • 3
    • 0036643079 scopus 로고    scopus 로고
    • Metric-based methods for adaptive model selection and regularization
    • Schuurmans D., and Southey F. Metric-based methods for adaptive model selection and regularization. Mach. Learn. 48 (2002) 51-84
    • (2002) Mach. Learn. , vol.48 , pp. 51-84
    • Schuurmans, D.1    Southey, F.2
  • 4
    • 51649123806 scopus 로고    scopus 로고
    • O. Bousquet, O. Chapelle, M. Hein, Measure based regularization, in: Neural Information Processing Symposium, Vancouver, British Columbia, Canada, 2003.
    • O. Bousquet, O. Chapelle, M. Hein, Measure based regularization, in: Neural Information Processing Symposium, Vancouver, British Columbia, Canada, 2003.
  • 5
    • 51649100323 scopus 로고    scopus 로고
    • M. Belkin, P. Niyogi, V. Sindhwani, Manifold regularization: a geometric framework for learning from examples, Technical Report TR-2004-06, Department of Computer Science, University of Chicago, 2004.
    • M. Belkin, P. Niyogi, V. Sindhwani, Manifold regularization: a geometric framework for learning from examples, Technical Report TR-2004-06, Department of Computer Science, University of Chicago, 2004.
  • 6
    • 23244434257 scopus 로고    scopus 로고
    • Learning the kernel function via regularization
    • Micchelli C.A., and Pontil M. Learning the kernel function via regularization. J. Mach. Learn. Res. 6 (2005) 1099-1125
    • (2005) J. Mach. Learn. Res. , vol.6 , pp. 1099-1125
    • Micchelli, C.A.1    Pontil, M.2
  • 7
    • 33947245952 scopus 로고    scopus 로고
    • Value regularization and fenchel duality
    • Rifkin R.M., and Lippert R.A. Value regularization and fenchel duality. J. Mach. Learn. Res. 8 (2007) 441-479
    • (2007) J. Mach. Learn. Res. , vol.8 , pp. 441-479
    • Rifkin, R.M.1    Lippert, R.A.2
  • 8
    • 14344256768 scopus 로고    scopus 로고
    • On information regularization
    • Corduneanu A., and Jaakkola T. On information regularization. UAI (2003)
    • (2003) UAI
    • Corduneanu, A.1    Jaakkola, T.2
  • 9
    • 38349178797 scopus 로고    scopus 로고
    • Classifier learning with a new locality regularization method
    • Xue H., Chen S., and Zeng X. Classifier learning with a new locality regularization method. Pattern Recognition 41 5 (2008) 1496-1507
    • (2008) Pattern Recognition , vol.41 , Issue.5 , pp. 1496-1507
    • Xue, H.1    Chen, S.2    Zeng, X.3
  • 10
    • 33745465341 scopus 로고    scopus 로고
    • M. Belkin, P. Niyogi, V. Sindhwani, On mainfold regularization, in: Proceedings of the International Workshop on Artificial Intelligence and Statistics, 2005.
    • M. Belkin, P. Niyogi, V. Sindhwani, On mainfold regularization, in: Proceedings of the International Workshop on Artificial Intelligence and Statistics, 2005.
  • 11
    • 0025490985 scopus 로고    scopus 로고
    • T. Poggio, F. Girosi, Networks for approximation and learning, in: Proceedings of the IEEE, vol. 78, 1990, pp. 1481-1497.
    • T. Poggio, F. Girosi, Networks for approximation and learning, in: Proceedings of the IEEE, vol. 78, 1990, pp. 1481-1497.
  • 12
    • 0025056697 scopus 로고
    • Regularization algorithms for learning that are equivalent to multilayer networks
    • Poggio T., and Girosi F. Regularization algorithms for learning that are equivalent to multilayer networks. Science 247 (1990) 978-982
    • (1990) Science , vol.247 , pp. 978-982
    • Poggio, T.1    Girosi, F.2
  • 13
    • 51649086153 scopus 로고    scopus 로고
    • A.R. Barron, Complexity regularization with application to artificial neural networks, in: G. Roussas (Eds.), Nonparametric Functional Estimation and Related Topics, 1991, pp. 561-576.
    • A.R. Barron, Complexity regularization with application to artificial neural networks, in: G. Roussas (Eds.), Nonparametric Functional Estimation and Related Topics, 1991, pp. 561-576.
  • 14
    • 33750730938 scopus 로고    scopus 로고
    • J.J. Pan, Q. Yang, H. Chang, D.-Y. Yeung. A manifold regularization approach to calibration reduction for sensor-network based tracking, in: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 06), 2006, pp. 988-993.
    • J.J. Pan, Q. Yang, H. Chang, D.-Y. Yeung. A manifold regularization approach to calibration reduction for sensor-network based tracking, in: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 06), 2006, pp. 988-993.
  • 17
    • 0000043665 scopus 로고
    • On solving incorrectly posed problems and method of regularization
    • Tikhonov A.N. On solving incorrectly posed problems and method of regularization. Dokl. Akad. Nauk USSR 151 (1963) 501-504
    • (1963) Dokl. Akad. Nauk USSR , vol.151 , pp. 501-504
    • Tikhonov, A.N.1
  • 20
    • 51649108394 scopus 로고    scopus 로고
    • G. Wahba, Spline Models for Observational Data, CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 59, Society for Industrial & Applied Mathematics, Philadelphia, PA, 1990.
    • G. Wahba, Spline Models for Observational Data, CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 59, Society for Industrial & Applied Mathematics, Philadelphia, PA, 1990.
  • 21
    • 51649088257 scopus 로고    scopus 로고
    • R.M. Rifkin. Everything old is new again: a fresh look at historical approaches to machine learning, Ph.D. Thesis, Massachusetts Institute of Technology, 2002.
    • R.M. Rifkin. Everything old is new again: a fresh look at historical approaches to machine learning, Ph.D. Thesis, Massachusetts Institute of Technology, 2002.
  • 22
    • 0000249788 scopus 로고    scopus 로고
    • An equivalence between sparse approximation and support vector machines
    • Girosi F. An equivalence between sparse approximation and support vector machines. Neural Comput. 10 6 (1998) 1455-1480
    • (1998) Neural Comput. , vol.10 , Issue.6 , pp. 1455-1480
    • Girosi, F.1
  • 23
    • 84865131152 scopus 로고    scopus 로고
    • B. Scholkopf, R. Herbrich, A.J. Smola. A generalized representer theorem, in: Proceedings of the 14th Annual Conference on Computational Learning Theory, 2001, pp. 416-426.
    • B. Scholkopf, R. Herbrich, A.J. Smola. A generalized representer theorem, in: Proceedings of the 14th Annual Conference on Computational Learning Theory, 2001, pp. 416-426.
  • 25
    • 0032638628 scopus 로고    scopus 로고
    • Least squares support vector machine classifiers
    • Suykens J.A.K., and Vandewalle J. Least squares support vector machine classifiers. Neural Process. Lett. 9 (1999) 293-300
    • (1999) Neural Process. Lett. , vol.9 , pp. 293-300
    • Suykens, J.A.K.1    Vandewalle, J.2
  • 26
    • 0034419669 scopus 로고    scopus 로고
    • Regularization networks and support vector machines
    • Evgeniou T., Pontil M., and Poggio T. Regularization networks and support vector machines. Adv. Comput. Math. 13 1 (2000) 1-50
    • (2000) Adv. Comput. Math. , vol.13 , Issue.1 , pp. 1-50
    • Evgeniou, T.1    Pontil, M.2    Poggio, T.3
  • 27
    • 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
  • 28
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis S.T., and Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290 22 (2000) 2323-2326
    • (2000) Science , vol.290 , Issue.22 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 29
    • 51649122556 scopus 로고    scopus 로고
    • M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral technique for embedding and clustering, in: Neural Information Processing Systems, vol.15, Vancouver, British Columbia, Canada, 2001.
    • M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral technique for embedding and clustering, in: Neural Information Processing Systems, vol.15, Vancouver, British Columbia, Canada, 2001.
  • 30
    • 33745881038 scopus 로고    scopus 로고
    • X. He, D. Cai, S. Yan, H. Zhang, Neighborhood preserving embedding, in: Proceedings of the 10th IEEE International Conference on Computer Vision, 2005.
    • X. He, D. Cai, S. Yan, H. Zhang, Neighborhood preserving embedding, in: Proceedings of the 10th IEEE International Conference on Computer Vision, 2005.
  • 31
    • 51649101395 scopus 로고    scopus 로고
    • X. He, P. Niyogi, Locality Preserving Projection, Neural Information Processing Symposium, Vancouver, British Columbia, Canada, 2003.
    • X. He, P. Niyogi, Locality Preserving Projection, Neural Information Processing Symposium, Vancouver, British Columbia, Canada, 2003.
  • 32
    • 84880899766 scopus 로고    scopus 로고
    • D. Cai, X. He, K. Zhou, J. Han, H. Bao, Locality sensitive discriminant analysis, in: International Joint Conference on Artificial Intelligence, 2007, pp. 708-713.
    • D. Cai, X. He, K. Zhou, J. Han, H. Bao, Locality sensitive discriminant analysis, in: International Joint Conference on Artificial Intelligence, 2007, pp. 708-713.
  • 34
    • 24644496298 scopus 로고    scopus 로고
    • H. Chen, H. Chang, T. Liu, Local discriminant embedding and its variants, in: International Conference on Computer Vision Pattern Recognition, 2005.
    • H. Chen, H. Chang, T. Liu, Local discriminant embedding and its variants, in: International Conference on Computer Vision Pattern Recognition, 2005.
  • 35
    • 51649094369 scopus 로고    scopus 로고
    • S. Szedmak, J. Shawe-Taylor, Muticlass learning at one-class complexity, Technical Report 1508, School of Electronics and Computer Science, Southampton, UK, 2005.
    • S. Szedmak, J. Shawe-Taylor, Muticlass learning at one-class complexity, Technical Report 1508, School of Electronics and Computer Science, Southampton, UK, 2005.
  • 36
    • 33144458972 scopus 로고    scopus 로고
    • Efficient and robust feature extraction by maximun margin criterion
    • Li H., Jiang T., and Zhang K. Efficient and robust feature extraction by maximun margin criterion. IEEE Trans. Neural Networks 17 1 (2006) 157-165
    • (2006) IEEE Trans. Neural Networks , vol.17 , Issue.1 , pp. 157-165
    • Li, H.1    Jiang, T.2    Zhang, K.3
  • 38
    • 51649106240 scopus 로고    scopus 로고
    • F.R.K. Chung, Spectral Graph Theory, in: Regional Conference Series in Mathematics, vol. 92, 1997.
    • F.R.K. Chung, Spectral Graph Theory, in: Regional Conference Series in Mathematics, vol. 92, 1997.
  • 39
    • 0037686659 scopus 로고    scopus 로고
    • The concave-convex procedure
    • Yuille A.L., and Rangarajan A. The concave-convex procedure. Neural Comput. 15 (2003) 915-936
    • (2003) Neural Comput. , vol.15 , pp. 915-936
    • Yuille, A.L.1    Rangarajan, A.2
  • 40
    • 33747105621 scopus 로고    scopus 로고
    • R. Collobert, F. Sinz, J. Weston, L. Bottou, Trading convexity for scalability, in: International Conference on Machine Learning, 2006.
    • R. Collobert, F. Sinz, J. Weston, L. Bottou, Trading convexity for scalability, in: International Conference on Machine Learning, 2006.
  • 41
    • 33749240206 scopus 로고    scopus 로고
    • Multi-class pattern classification using neural networks
    • Ou G., and Murphey Y.L. Multi-class pattern classification using neural networks. Pattern Recognition 40 1 (2007) 4-18
    • (2007) Pattern Recognition , vol.40 , Issue.1 , pp. 4-18
    • Ou, G.1    Murphey, Y.L.2
  • 42
    • 34547995832 scopus 로고    scopus 로고
    • S. Asharaf, M.N. Murty, S.K. Shevade, Multiclass core vector machine, in: International Conference on Machine Learning, Corvallis, OR, 2007.
    • S. Asharaf, M.N. Murty, S.K. Shevade, Multiclass core vector machine, in: International Conference on Machine Learning, Corvallis, OR, 2007.
  • 43
    • 15344339935 scopus 로고    scopus 로고
    • Optimizing the kernel in the empirical feature space
    • Xiong H., Swamy M.N.S., and Ahmad M.O. Optimizing the kernel in the empirical feature space. IEEE Trans. Neural Networks 16 2 (2005) 460-474
    • (2005) IEEE Trans. Neural Networks , vol.16 , Issue.2 , pp. 460-474
    • Xiong, H.1    Swamy, M.N.S.2    Ahmad, M.O.3
  • 44
    • 33644830072 scopus 로고    scopus 로고
    • Multisurface proximal support vector machine classification via generalized eigenvalues
    • Mangasarian O., and Wild E. Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans. Pattern Anal. Mach. Intell. 28 1 (2006) 69-74
    • (2006) IEEE Trans. Pattern Anal. Mach. Intell. , vol.28 , Issue.1 , pp. 69-74
    • Mangasarian, O.1    Wild, E.2
  • 45
    • 37549013404 scopus 로고    scopus 로고
    • MultiK-MHKS: a novel multiple kernel learning algorithm
    • Wang Z., Chen S., and Sun T. MultiK-MHKS: a novel multiple kernel learning algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 30 2 (2008) 348-353
    • (2008) IEEE Trans. Pattern Anal. Mach. Intell. , vol.30 , Issue.2 , pp. 348-353
    • Wang, Z.1    Chen, S.2    Sun, T.3
  • 48
    • 51649129362 scopus 로고    scopus 로고
    • C.A. Micchelli, M. Pontil, Kernels for multi-task learning, in: Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2004.
    • C.A. Micchelli, M. Pontil, Kernels for multi-task learning, in: Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2004.
  • 49
    • 14544299611 scopus 로고    scopus 로고
    • On learning vector-valued functions
    • Micchelli C.A., and Pontil M. On learning vector-valued functions. Neural Comput. 17 (2005) 177-204
    • (2005) Neural Comput. , vol.17 , pp. 177-204
    • Micchelli, C.A.1    Pontil, M.2
  • 50
    • 2342517502 scopus 로고    scopus 로고
    • Think globally, fit locally: unsupervised learning of low dimensional manifolds
    • Saul L.K., and Roweis S.T. Think globally, fit locally: unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res. 4 (2003) 119-155
    • (2003) J. Mach. Learn. Res. , vol.4 , pp. 119-155
    • Saul, L.K.1    Roweis, S.T.2
  • 51
    • 24644474150 scopus 로고    scopus 로고
    • A. Ghodsi, J. Huang, F. Southey, D. Schuurmans, Tangent-corrected embedding, CVPR, 2005.
    • A. Ghodsi, J. Huang, F. Southey, D. Schuurmans, Tangent-corrected embedding, CVPR, 2005.
  • 52
    • 51649111011 scopus 로고    scopus 로고
    • A.M. Martinez, R. Benavente, The AR Face Database, CVC Technical Report #24, June, 1998.
    • A.M. Martinez, R. Benavente, The AR Face Database, CVC Technical Report #24, June, 1998.
  • 53
    • 51649112485 scopus 로고    scopus 로고
    • S.A. Nene, S.K. Nayar, H. Murase, Columbia Object Image Library (COIL-20), Technical Report CUCS-005-96, February, 1996.
    • S.A. Nene, S.K. Nayar, H. Murase, Columbia Object Image Library (COIL-20), Technical Report CUCS-005-96, February, 1996.
  • 54
    • 33847056269 scopus 로고    scopus 로고
    • Locality preserving CCA with applications to data visualization and pose estimation
    • Sun T., and Chen S. Locality preserving CCA with applications to data visualization and pose estimation. Image Vision Comput. 25 5 (2007) 531-543
    • (2007) Image Vision Comput. , vol.25 , Issue.5 , pp. 531-543
    • Sun, T.1    Chen, S.2
  • 55
    • 84864060454 scopus 로고    scopus 로고
    • A. Argyriou, M. Herbster, M. Pontil, Combing graph Laplacians for semi-supervised learning, Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2005.
    • A. Argyriou, M. Herbster, M. Pontil, Combing graph Laplacians for semi-supervised learning, Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2005.
  • 56
    • 1942517297 scopus 로고    scopus 로고
    • X.Z. Fern, C.E. Brodley, Random projection for high dimensional data clustering: a cluster ensemble approach, in: International Conference on Machine Learning, 2003, pp. 186-193.
    • X.Z. Fern, C.E. Brodley, Random projection for high dimensional data clustering: a cluster ensemble approach, in: International Conference on Machine Learning, 2003, pp. 186-193.
  • 57
    • 51649126816 scopus 로고    scopus 로고
    • X.Z. Fern, C.E. Brodley, Cluster ensembles for high dimensional clustering: an empirical study, Technical Report CS06-30-02, Oregon State University, 2006.
    • X.Z. Fern, C.E. Brodley, Cluster ensembles for high dimensional clustering: an empirical study, Technical Report CS06-30-02, Oregon State University, 2006.
  • 58
    • 51649118156 scopus 로고    scopus 로고
    • S. Durand, M. Nikolova, Stability of minimizers of regularized least squares objective functions I: study of the local behavior, Technical Report TSI-ENST, Paris, France, 2001.
    • S. Durand, M. Nikolova, Stability of minimizers of regularized least squares objective functions I: study of the local behavior, Technical Report TSI-ENST, Paris, France, 2001.
  • 59
    • 51649092215 scopus 로고    scopus 로고
    • S. Durand, M. Nikolova. Stability of minimizers of regularized least squares objective functions II: study of the global behavior, Technical Report TSI-ENST, Paris, France, 2001.
    • S. Durand, M. Nikolova. Stability of minimizers of regularized least squares objective functions II: study of the global behavior, Technical Report TSI-ENST, Paris, France, 2001.
  • 60
    • 33749630481 scopus 로고    scopus 로고
    • R.H. Chan, C.-W. Ho, C.-Y. Leung, M. Nikolova, Minimization of detail-preserving regularization functional by Newtons method with continuation, in: International Conference on Image Processing, 2005, pp. 125-128.
    • R.H. Chan, C.-W. Ho, C.-Y. Leung, M. Nikolova, Minimization of detail-preserving regularization functional by Newtons method with continuation, in: International Conference on Image Processing, 2005, pp. 125-128.
  • 61
    • 27744554422 scopus 로고    scopus 로고
    • Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
    • Chan R.H., Ho C.-W., and Nikolova M. Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans. Image Process. 14 10 (2005) 1479-1485
    • (2005) IEEE Trans. Image Process. , vol.14 , Issue.10 , pp. 1479-1485
    • Chan, R.H.1    Ho, C.-W.2    Nikolova, M.3
  • 62
    • 51649101394 scopus 로고    scopus 로고
    • I.W. Tsang, J.T. Kwok, Large-scale sparsified manifold regularization, in: Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2006.
    • I.W. Tsang, J.T. Kwok, Large-scale sparsified manifold regularization, in: Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2006.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.