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Volumn 26, Issue 8, 2003, Pages 990-996

Interpolation based kernel function' s construction

Author keywords

Interpolation; Kernel function; Pattern recognition; SLT; SVM

Indexed keywords

FUNCTIONS; INTERPOLATION; LEARNING ALGORITHMS; THEOREM PROVING; VECTORS;

EID: 0344036310     PISSN: 02544164     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (17)

References (12)
  • 1
    • 0003710382 scopus 로고    scopus 로고
    • On the influence of the kernel on the generalization ability of support vector machines
    • Department of Mathematics and Computer Science, Friedrich Schiller University, (Jena): Technical Report TR-01-01
    • Ingo Steinwart, On the influence of the kernel on the generalization ability of support vector machines. Department of mathematics and computer science, Friedrich Schiller University (Jena): Technical Report TR-01-01, 2001 (Available as http://www.minet.uni-jena.de/Math-Net/reports/rep-con.html)
    • (2001)
    • Steinwart, I.1
  • 2
    • 0032786569 scopus 로고    scopus 로고
    • Improving support vector machine classifiers by modifying kernel functions
    • Shun-ichi Amari, Si Wu. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 1999, 12: 783-789.
    • (1999) Neural Networks , vol.12 , pp. 783-789
    • Amari, S.-I.1    Wu, S.2
  • 4
    • 0003893955 scopus 로고    scopus 로고
    • Support vector learning
    • [Ph D dissertation], Berlin University, Berlin
    • Scholkpf B. Support vector learning [Ph D dissertation]. Berlin University, Berlin, 1997.
    • (1997)
    • Scholkpf, B.1
  • 8
    • 0034264380 scopus 로고    scopus 로고
    • Bounds on error expectation for support vector machines
    • Vapnik V, Chapelle O. Bounds on error expectation for support vector machines. Neural Computation, 2000, 12(9): 2013-2036.
    • (2000) Neural Computation , vol.12 , Issue.9 , pp. 2013-2036
    • Vapnik, V.1    Chapelle, O.2
  • 9
    • 0003262517 scopus 로고    scopus 로고
    • Model selection for support vector machines
    • Solla S.A., Leen T.K. and Muller K.R.(ed.), Cambridge: The MIT Press
    • Chapella O, Vapnik V. Model selection for support vector machines. In: Solla S A, Leen T K, Muller K R eds. Advances in Neural Information Processing Systems. Cambridge: The MIT Press, 1999.
    • (1999) Advances in Neural Information Processing Systems
    • Chapella, O.1    Vapnik, V.2
  • 10
    • 0036161011 scopus 로고    scopus 로고
    • Choosing multiple parameters for support vector machines
    • Chapella O, Vapnik V. Choosing multiple parameters for support vector machines. Machine Learning, 2002, 46(1): 131-159.
    • (2002) Machine Learning , vol.46 , Issue.1 , pp. 131-159
    • Chapella, O.1    Vapnik, V.2
  • 11
    • 25044438999 scopus 로고    scopus 로고
    • Bayesian inference in support vector regression
    • National University of Singapore: Technical Report CD-01-15
    • Wei Chu, Keerthi S S, Chong Jin Ong. Bayesian inference in support vector regression. National University of Singapore: Technical Report CD-01-15, 2001.
    • (2001)
    • Wei, C.1    Keerthi, S.S.2    Chong, O.J.3
  • 12
    • 0035242302 scopus 로고    scopus 로고
    • The theory of SVM and programming base learning algorithms in neural networks
    • Chinese source
    • Zhang Ling, Zhang Bo. The theory of SVM and programming base learning algorithms in neural networks. Chinese Journal of Computers, 2001, 24(2): 113-118 (in Chinese)
    • (2001) Chinese Journal of Computers , vol.24 , Issue.2 , pp. 113-118
    • Zhang, L.1    Zhang, B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.