메뉴 건너뛰기




Volumn , Issue , 2012, Pages

Multi-objective optimization and Meta-learning for SVM parameter selection

Author keywords

[No Author keywords available]

Indexed keywords

INITIAL SOLUTION; INPUT PROBLEM; LEARNING PROBLEM; METALEARNING; MULTI OBJECTIVE; MULTI OBJECTIVE OPTIMIZATIONS (MOO); MULTI-OBJECTIVE OPTIMIZATION PROBLEM; OPTIMIZATION PROBLEMS; PARAMETER SELECTION; SEARCH ALGORITHMS; SEARCH PROCESS; SEARCH SPACES; SINGLE OBJECTIVE; SUPPORT VECTOR;

EID: 84865094425     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252378     Document Type: Conference Paper
Times cited : (14)

References (33)
  • 3
    • 84898998301 scopus 로고    scopus 로고
    • Dynamically adapting kernels in support vector machines
    • N. Cristianini, C. Campbell, and J. Shawe-Taylor. Dynamically adapting kernels in support vector machines. NIPS, 204-210, 1998.
    • (1998) NIPS , pp. 204-210
    • Cristianini, N.1    Campbell, C.2    Shawe-Taylor, J.3
  • 4
    • 85013615152 scopus 로고    scopus 로고
    • On optimal degree selection for polynomial kernel with support vector machines: Theoretical and empirical investigations
    • S. Ali and K. Smith-Miles. On optimal degree selection for polynomial kernel with support vector machines: Theoretical and empirical investigations. KES Journal, 1(1):1-18, 2007.
    • (2007) KES Journal , vol.1 , Issue.1 , pp. 1-18
    • Ali, S.1    Smith-Miles, K.2
  • 7
    • 15844394276 scopus 로고    scopus 로고
    • Evolutionary tuning of multiple svm parameters
    • F. Friedrichs and C. Igel. Evolutionary tuning of multiple svm parameters. Neurocomputing, 2005.
    • (2005) Neurocomputing
    • Friedrichs, F.1    Igel, C.2
  • 8
    • 84863973992 scopus 로고    scopus 로고
    • Evolutionary tuning of svm parameter values in multiclass problems
    • A. Lorena and A. de Carvalho. Evolutionary tuning of svm parameter values in multiclass problems. Neurocomputing, pages 16-18.
    • Neurocomputing , pp. 16-18
    • Lorena, A.1    De Carvalho, A.2
  • 9
    • 0036738840 scopus 로고    scopus 로고
    • Efficient tuning of svm hyperparameters using radius/ margin bound and iterative algorithms
    • S.S. Keerthi. Efficient tuning of svm hyperparameters using radius/ margin bound and iterative algorithms. IEEE Transactions on Neural Networks, 2002.
    • (2002) IEEE Transactions on Neural Networks
    • Keerthi, S.S.1
  • 10
    • 1642405080 scopus 로고    scopus 로고
    • Exploiting sampling and meta-learning for parameter setting support vector machines
    • P. Kuba, P. Brazdil, C. Soares, and A. Woznica. Exploiting sampling and meta-learning for parameter setting support vector machines. Proceedings of the IBERAMIA, pages 217-225, 2001.
    • (2001) Proceedings of the IBERAMIA , pp. 217-225
    • Kuba, P.1    Brazdil, P.2    Soares, C.3    Woznica, A.4
  • 15
    • 23944487822 scopus 로고    scopus 로고
    • Gradient-based adaptation of general gaussian kernels
    • T. Glasmachers and C. Igel. Gradient-based adaptation of general gaussian kernels. Neural Comput., 2005.
    • (2005) Neural Comput.
    • Glasmachers, T.1    Igel, C.2
  • 18
    • 43449135303 scopus 로고    scopus 로고
    • Pareto-based multi-objective machine learning : An overview and case studies
    • Y. Jin and B. Sendhoff, Pareto-Based Multi-Objective Machine Learning : An Overview and Case Studies, IEEE Transactions on Systems, Man and Cybernetics: Part C, vol. 38, no. 3, pp. 397-415, 2008.
    • (2008) IEEE Transactions on Systems, Man and Cybernetics: Part C , vol.38 , Issue.3 , pp. 397-415
    • Jin, Y.1    Sendhoff, B.2
  • 19
    • 33750292535 scopus 로고    scopus 로고
    • A meta-learning approach to automatic kernel selection for support vector machines
    • S. Ali and K. A. Smith-Miles. A meta-learning approach to automatic kernel selection for support vector machines. Neurocomputing, 173-186, 2006.
    • (2006) Neurocomputing , pp. 173-186
    • Ali, S.1    Smith-Miles, K.A.2
  • 20
    • 33751067274 scopus 로고    scopus 로고
    • Selecting parameters of svm using metalearning and kernel matrix-based meta-features
    • C. Soares and P. Brazdil. Selecting parameters of svm using metalearning and kernel matrix-based meta-features. SAC, 2006.
    • (2006) SAC
    • Soares, C.1    Brazdil, P.2
  • 25
    • 84863973994 scopus 로고    scopus 로고
    • Asymptotic behaviors of support vector machines with gaussian kernel
    • Kluwer Academic Publishers
    • S. S. Keerthi and C.-J. Lin. Asymptotic behaviors of support vector machines with gaussian kernel. In Tomasz Imielinski and Hank Korth, editors, Mobile Computing, pages 153-181. Kluwer Academic Publishers, 1996.
    • (1996) Tomasz Imielinski and Hank Korth, Editors, Mobile Computing , pp. 153-181
    • Keerthi, S.S.1    Lin, C.-J.2
  • 27
    • 84865064413 scopus 로고    scopus 로고
    • WEKA
    • WEKA, The University of Waikato, Available: http://www.cs.waikato.ac.nz/ ml/weka/.
  • 28
    • 36948999941 scopus 로고    scopus 로고
    • Irvine, CA: University of California, School of Information and Computer Science
    • Frank, A. Asuncion, A. UCI Machine Learning Repository, Avaiable: http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science.
    • A. UCI Machine Learning Repository
    • Asuncion, F.A.1
  • 32
    • 4344699771 scopus 로고    scopus 로고
    • Comparison of multiobjective evolutionary algorithms: Empirical results
    • E. Zitzler, K. Deb, and L. Thiele, "Comparison of multiobjective evolutionary algorithms: Empirical results," Evolutionary Computation Journal, vol. 8(2), pp. 125-148, 2000.
    • (2000) Evolutionary Computation Journal , vol.8 , Issue.2 , pp. 125-148
    • Zitzler, E.1    Deb, K.2    Thiele, L.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.