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Volumn 29, Issue 2, 1996, Pages 341-348

Analysis of decision boundaries in linearly combined neural classifiers

Author keywords

Combining; Decision boundary; Hybrid networks; Neural networks; Pattern classification; Variance reduction

Indexed keywords

DECISION THEORY; ERROR ANALYSIS; MATHEMATICAL MODELS; NEURAL NETWORKS; PARAMETER ESTIMATION;

EID: 0030085913     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/0031-3203(95)00085-2     Document Type: Article
Times cited : (273)

References (21)
  • 1
    • 0025508916 scopus 로고
    • A statistical approach to learning and generalization in layered neural networks
    • October
    • E. Levin, N. Tishby and S. A. Solla, A statistical approach to learning and generalization in layered neural networks, Proc. IEEE 78(10), 1568-1574 (October 1990).
    • (1990) Proc. IEEE , vol.78 , Issue.10 , pp. 1568-1574
    • Levin, E.1    Tishby, N.2    Solla, S.A.3
  • 2
    • 0000206819 scopus 로고
    • A mathematical theory of generalization
    • D. H. Wolpert, A mathematical theory of generalization, Complex Syst. 4, 151-200 (1990).
    • (1990) Complex Syst. , vol.4 , pp. 151-200
    • Wolpert, D.H.1
  • 3
    • 0001942829 scopus 로고
    • Neural networks and the bias/variance dilemma
    • S. Geman, E. Bienenstock and R. Doursat, Neural networks and the bias/variance dilemma, Neural Comput. 4(1), 1-58 (1992).
    • (1992) Neural Comput. , vol.4 , Issue.1 , pp. 1-58
    • Geman, S.1    Bienenstock, E.2    Doursat, R.3
  • 6
    • 0000703707 scopus 로고
    • Structural adaptation and generalization in supervised feed-forward networks
    • J. Ghosh and K. Turner, Structural adaptation and generalization in supervised feed-forward networks, J. Artif. Neural Networks 1(4), 431-458 (1994).
    • (1994) J. Artif. Neural Networks , vol.1 , Issue.4 , pp. 431-458
    • Ghosh, J.1    Turner, K.2
  • 7
    • 0026692226 scopus 로고
    • Stacked generalization
    • D. H. Wolpert, Stacked generalization, Neural Networks 5, 241-259 (1992).
    • (1992) Neural Networks , vol.5 , pp. 241-259
    • Wolpert, D.H.1
  • 8
    • 0000494468 scopus 로고
    • Synergy of clustering multiple back propagation networks
    • W. P. Lincoln and J. Skrzypek, Synergy of clustering multiple back propagation networks, Adv. Neural Inf. Process. Syst. 2, 650-657 (1990).
    • (1990) Adv. Neural Inf. Process. Syst. , vol.2 , pp. 650-657
    • Lincoln, W.P.1    Skrzypek, J.2
  • 9
    • 0000926506 scopus 로고
    • When networks disagree: Ensemble methods for hybrid neural networks
    • R. J. Mammone, ed. Chapman-Hall, New York
    • M. P. Perrone and L. N. Cooper, When networks disagree: Ensemble methods for hybrid neural networks, Neural Networks for Speech and Image Processing, R. J. Mammone, ed. Chapman-Hall, New York (1993).
    • (1993) Neural Networks for Speech and Image Processing
    • Perrone, M.P.1    Cooper, L.N.2
  • 10
    • 0000939726 scopus 로고
    • A framework for estimating performance improvements in hybrid pattern classifiers
    • INNS Press
    • K. Tumer and J. Ghosh, A framework for estimating performance improvements in hybrid pattern classifiers, Proc. of the World Congress Neural Networks, Vol. III, pp. 220-225. INNS Press (1994).
    • (1994) Proc. of the World Congress Neural Networks , vol.3 , pp. 220-225
    • Tumer, K.1    Ghosh, J.2
  • 11
    • 0026993851 scopus 로고
    • Evidence combination techniques for robust classification of short-duration oceanic signals
    • Orlando, Florida April
    • J. Ghosh, S. Beck and C. C. Chu, Evidence combination techniques for robust classification of short-duration oceanic signals, SPIE Conf. Adapt. Learning Syst. SPIC Proc. Vol. 1706, pp. 266-276. Orlando, Florida (April 1992).
    • (1992) SPIE Conf. Adapt. Learning Syst. SPIC Proc. , vol.1706 , pp. 266-276
    • Ghosh, J.1    Beck, S.2    Chu, C.C.3
  • 12
    • 33745718052 scopus 로고    scopus 로고
    • Integration of neural classifiers for passive sonar signals
    • C. T. Leondes, ed. Academic Press, New York (to be published)
    • J. Ghosh, K. Turner, S. Beck and L. Deuser, Integration of neural classifiers for passive sonar signals, DSP Theory and Applications. C. T. Leondes, ed. Academic Press, New York (to be published).
    • DSP Theory and Applications
    • Ghosh, J.1    Turner, K.2    Beck, S.3    Deuser, L.4
  • 13
    • 0026860706 scopus 로고
    • Methods of combining multiple classifiers and their applications to handwriting recognition
    • May
    • L. Xu, A. Krzyzak and C. Y. Suen, Methods of combining multiple classifiers and their applications to handwriting recognition, IEEE Trans. Syst. Man Cybernet. 22(3), 418-435 (May 1992).
    • (1992) IEEE Trans. Syst. Man Cybernet. , vol.22 , Issue.3 , pp. 418-435
    • Xu, L.1    Krzyzak, A.2    Suen, C.Y.3
  • 14
    • 0027961797 scopus 로고
    • Combining the results of several neural network classifiers
    • G. Rogova, Combining the results of several neural network classifiers, Neural Networks 7(5), 777-781 (1994).
    • (1994) Neural Networks , vol.7 , Issue.5 , pp. 777-781
    • Rogova, G.1
  • 15
    • 0022823858 scopus 로고
    • Probabilistic interpretation for MY-CIN's uncertainty factors
    • L. N. Kanal and J. F. Lemmer, eds, North-Holland, Amsterdam
    • D. Heckerman, Probabilistic interpretation for MY-CIN's uncertainty factors, Uncertainty in Artificial Intelligence, L. N. Kanal and J. F. Lemmer, eds, pp. 167-196. North-Holland, Amsterdam (1986).
    • (1986) Uncertainty in Artificial Intelligence , pp. 167-196
    • Heckerman, D.1
  • 16
    • 0001595997 scopus 로고
    • Neural network classifiers estimate Bayesian a posteriori probabilities
    • M. D. Richard and R. P. Lippmann, Neural network classifiers estimate Bayesian a posteriori probabilities, Neural Comput. 3(4), 461-483 (1991).
    • (1991) Neural Comput. , vol.3 , Issue.4 , pp. 461-483
    • Richard, M.D.1    Lippmann, R.P.2
  • 17
    • 0037785620 scopus 로고
    • Least squares learning and approximation of posterior probabilities on classification problems by neural network models
    • WNN-AIND91, Auburn February
    • P. A. Shoemaker, M. J. Carlin, R. L. Shimabukuro and C. E. Priebe, Least squares learning and approximation of posterior probabilities on classification problems by neural network models, Proc. 2nd Workshop Neural Networks, pp. 187-196. WNN-AIND91, Auburn (February 1991).
    • (1991) Proc. 2nd Workshop Neural Networks , pp. 187-196
    • Shoemaker, P.A.1    Carlin, M.J.2    Shimabukuro, R.L.3    Priebe, C.E.4
  • 20
    • 0026771388 scopus 로고
    • Hybrid system for protein secondary structure prediction
    • X. Zhang, J. P. Mesirov and D. L. Waltz, Hybrid system for protein secondary structure prediction, J. Mol. Biol. 225, 1049-1063 (1992).
    • (1992) J. Mol. Biol. , vol.225 , pp. 1049-1063
    • Zhang, X.1    Mesirov, J.P.2    Waltz, D.L.3
  • 21
    • 0043010195 scopus 로고
    • Boundary variance reduction for improved classification through hybrid networks
    • April
    • K. Turner and J. Ghosh, Boundary variance reduction for improved classification through hybrid networks (Invited paper), Applications and Science of Artificial Neural Networks, Proc. SPIE, Vol. 2492, pp. 573-584 (April 1995).
    • (1995) Applications and Science of Artificial Neural Networks, Proc. SPIE , vol.2492 , pp. 573-584
    • Turner, K.1    Ghosh, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.