메뉴 건너뛰기




Volumn 3610, Issue PART I, 2005, Pages 546-553

An information-geometrical approach to constructing kernel in support vector regression machines

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); FUNCTIONS; GEOMETRY; PROBLEM SOLVING;

EID: 26844550498     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11539087_70     Document Type: Conference Paper
Times cited : (3)

References (14)
  • 2
    • 34249753618 scopus 로고
    • Support vector networks
    • Cortes, C., Vapnik, V.: Support Vector Networks. Machine learning. 20 (1995) 273-297
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 4
    • 84898937307 scopus 로고    scopus 로고
    • Support vector method for multivariate density estimation
    • MIT Press, Cambridge, MA
    • Vapnik, V. et al.: Support Vector Method for Multivariate Density Estimation. Advances in Neural Information Processing System. MIT Press, Cambridge, MA. 12 (1999) 659-665
    • (1999) Advances in Neural Information Processing System , vol.12 , pp. 659-665
    • Vapnik, V.1
  • 5
    • 26844550191 scopus 로고    scopus 로고
    • Regression using support vector machines: Basic foundations
    • Electrical and Computer Engineering Department, University of Louisville
    • Aly, F., Refaat, M.: Regression Using Support Vector Machines: Basic Foundations. Technical Report. Electrical and Computer Engineering Department, University of Louisville. 2004
    • (2004) Technical Report
    • Aly, F.1    Refaat, M.2
  • 7
    • 2942746029 scopus 로고    scopus 로고
    • Learning and soft computing: Support vector machines
    • MIT Press, Cambridge, MA
    • Kecman, V.: Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press, Cambridge, MA. 2001
    • (2001) Neural Networks, and Fuzzy Logic Models
    • Kecman, V.1
  • 8
    • 1842430977 scopus 로고    scopus 로고
    • Sparse modeling using orthogonal forward regression with PRESS statistic and regularization
    • Chen, S. et al.: Sparse Modeling Using Orthogonal forward Regression with PRESS Statistic and Regularization. IEEE Trans. on Systems, Man and Cybernetics, Part B. 34 (2004) 898-911
    • (2004) IEEE Trans. on Systems, Man and Cybernetics, Part B , vol.34 , pp. 898-911
    • Chen, S.1
  • 9
    • 0032786569 scopus 로고    scopus 로고
    • Improving support vector machine classifiers by modifying kernel functions
    • Amari, S., Wu, S.: Improving Support Vector Machine Classifiers by Modifying Kernel Functions. Neural Networks. 12 (1999) 783-789
    • (1999) Neural Networks , vol.12 , pp. 783-789
    • Amari, S.1    Wu, S.2
  • 10
    • 0036469481 scopus 로고    scopus 로고
    • Conformal transformation of kernel functions: A data-dependent way to improve support vector machine classifiers
    • Wu, S., Amari, S.: Conformal Transformation of Kernel Functions: a Data-dependent Way to Improve Support Vector Machine Classifiers. Neural Processing Letters. 15 (2002) 59-67
    • (2002) Neural Processing Letters , vol.15 , pp. 59-67
    • Wu, S.1    Amari, S.2
  • 11
    • 0036825821 scopus 로고    scopus 로고
    • Kernel methods: A survey of current techniques
    • Colin, C.: Kernel Methods: A Survey of Current Techniques. Neurocomputing. 48 (2002) 63-48
    • (2002) Neurocomputing , vol.48 , pp. 63-148
    • Colin, C.1
  • 13
    • 4444224442 scopus 로고    scopus 로고
    • Support vector machines regression on-line modelling and its application
    • Wang, D.C.: Support Vector Machines Regression On-line Modelling and Its Application (In Chinese). Control and Decision. 18 (2003) 89-91
    • (2003) Control and Decision , vol.18 , pp. 89-91
    • Wang, D.C.1
  • 14
    • 3442887043 scopus 로고    scopus 로고
    • A real-time model for forecasting zinc output by support vector machines in imperial smelting furnace
    • Kunzhi, H.: A Real-time Model for Forecasting Zinc Output by Support Vector Machines in Imperial Smelting Furnace (In Chinese). Computer Engineering. 30 (2004) 16-18
    • (2004) Computer Engineering , vol.30 , pp. 16-18
    • Kunzhi, H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.