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Volumn 5342 LNCS, Issue , 2008, Pages 896-905

Outlier robust Gaussian process classification

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; ERRORS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LABELING; PATTERN RECOGNITION; SURFACE PLASMON RESONANCE; SYNTACTICS; TECHNICAL PRESENTATIONS; TRELLIS CODES;

EID: 58349104814     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89689-0_93     Document Type: Conference Paper
Times cited : (21)

References (12)
  • 3
    • 0004220749 scopus 로고    scopus 로고
    • Monte Carlo implementation of Gaussian process models for Bayesian regression and classification
    • Technical Report CRG-TR-97-2, Dept. of Computer Science, University of Toronto
    • Neal, R.: Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Technical Report CRG-TR-97-2, Dept. of Computer Science, University of Toronto (1997)
    • (1997)
    • Neal, R.1
  • 4
    • 0034320350 scopus 로고    scopus 로고
    • Gaussian processes for classification: Mean field algorithms
    • Opper, M., Winther, O.: Gaussian processes for classification: Mean field algorithms. Neural Computation 12, 2655-2684 (2000)
    • (2000) Neural Computation , vol.12 , pp. 2655-2684
    • Opper, M.1    Winther, O.2
  • 6
    • 58349095540 scopus 로고    scopus 로고
    • Williams, C.K.I., Rasmussen, C.E.: Gaussian processes for regression. In: NIPS, 8. MIT Press, Cambridge (1995)
    • Williams, C.K.I., Rasmussen, C.E.: Gaussian processes for regression. In: NIPS, vol. 8. MIT Press, Cambridge (1995)
  • 7
    • 33947142953 scopus 로고    scopus 로고
    • Sparse representation for Gaussian process models
    • Seeger, M., Lawrence, N., Herbrich, R.: Sparse representation for Gaussian process models. In: NIPS, vol. 15 (2002)
    • (2002) NIPS , vol.15
    • Seeger, M.1    Lawrence, N.2    Herbrich, R.3
  • 9
    • 0002788893 scopus 로고    scopus 로고
    • A view of the EM algorithm that justifies incremental, sparse, and other variants
    • Jordan, M.I, ed, Kluwer, Dordrecht
    • Neal, R., Hinton, G.: A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan, M.I. (ed.) Learning in Graphical Models. Kluwer, Dordrecht (1998)
    • (1998) Learning in Graphical Models
    • Neal, R.1    Hinton, G.2
  • 10
    • 0001025418 scopus 로고
    • Bayesian interpolation
    • MacKay, D.J.C.: Bayesian interpolation. Neural Computation 4(3), 415-447 (1992)
    • (1992) Neural Computation , vol.4 , Issue.3 , pp. 415-447
    • MacKay, D.J.C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.