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Volumn 12, Issue 6, 2000, Pages 1411-1427

Nonmonotonic generalization bias of gaussian mixture models

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; LEARNING; NORMAL DISTRIBUTION; TEMPERATURE;

EID: 0034202524     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976600300015439     Document Type: Article
Times cited : (17)

References (17)
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    • A convergence theorem for the fuzzy ISODATA clustering algorithms
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    • A competitive modular connectionist architecture
    • D. Touretzky & R. Lippmann (Eds.), San Mateo, CA: Morgan Kaufmann
    • Jacobs, R. A., & Jordan, M. I. (1991). A competitive modular connectionist architecture. In D. Touretzky & R. Lippmann (Eds.), Advances in neural information processing systems, 3 (pp. 767-773). San Mateo, CA: Morgan Kaufmann.
    • (1991) Advances in Neural Information Processing Systems , vol.3 , pp. 767-773
    • Jacobs, R.A.1    Jordan, M.I.2
  • 6
    • 0000262562 scopus 로고
    • Hierarchical mixtures of experts and the EM algorithm
    • Jordan, M. I., & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214.
    • (1994) Neural Computation , vol.6 , pp. 181-214
    • Jordan, M.I.1    Jacobs, R.A.2
  • 7
    • 0040269271 scopus 로고
    • Using Boltzmann machines for probability estimation: A general framework for neural network learning
    • S. Gielen et al. (Eds.), Berlin: Springer-Verlag
    • Kappen, B. (1993). Using Boltzmann machines for probability estimation: A general framework for neural network learning. In S. Gielen et al. (Eds.), Proc. of ICANN'93 (pp. 521-526). Berlin: Springer-Verlag.
    • (1993) Proc. of ICANN'93 , pp. 521-526
    • Kappen, B.1
  • 8
    • 0029025010 scopus 로고
    • Deterministic learning rules for Boltzmann machines
    • Kappen, B. (1995). Deterministic learning rules for Boltzmann machines. Neural Networks, 8, 537-548.
    • (1995) Neural Networks , vol.8 , pp. 537-548
    • Kappen, B.1
  • 9
    • 0000902690 scopus 로고
    • The effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems
    • J. E. Moody, S. J. Hanson, & R. P. Lippman (Eds.), San Mateo, CA: Morgan Kaufmann
    • Moody, J. (1992). The effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems. In J. E. Moody, S. J. Hanson, & R. P. Lippman (Eds.), Advances in neural information processing systems, 4 (pp. 847-854). San Mateo, CA: Morgan Kaufmann.
    • (1992) Advances in Neural Information Processing Systems , vol.4 , pp. 847-854
    • Moody, J.1
  • 10
    • 0040269272 scopus 로고
    • A criterion for determining the number of parameters in an artificial neural network model
    • T. Kohonen et al. (Eds.), Amsterdam: Elsevier
    • Murata, N., Yoshizawa, S., & Amari, S. (1991). A criterion for determining the number of parameters in an artificial neural network model. In T. Kohonen et al. (Eds.), Artificial neural network (ICANN) (pp. 9-14). Amsterdam: Elsevier.
    • (1991) Artificial Neural Network (ICANN) , pp. 9-14
    • Murata, N.1    Yoshizawa, S.2    Amari, S.3
  • 11
    • 0028544395 scopus 로고
    • Network information criterions -determining the number of parameters for an artificial neural network model
    • Murata, N., Yoshizawa, S., & Amari, S. (1994). Network information criterions -determining the number of parameters for an artificial neural network model. IEEE Trans. on Neural Networks, 5, 865-872.
    • (1994) IEEE Trans. on Neural Networks , vol.5 , pp. 865-872
    • Murata, N.1    Yoshizawa, S.2    Amari, S.3
  • 12
    • 0031151347 scopus 로고    scopus 로고
    • Symmetry breaking and training from incomplete data with radial basis Boltzmann machines
    • Nijman, M. J., & Kappen, H. J. (1997). Symmetry breaking and training from incomplete data with radial basis Boltzmann machines. International Journal of Neural Systems, 8, 301-316.
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    • Nijman, M.J.1    Kappen, H.J.2
  • 13
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    • Networks for approximation and learning
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  • 15
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    • Statistical mechanics of phase transitions in clustering
    • Rose, K., Gurewitz, E., & Fox, G. (1990). Statistical mechanics of phase transitions in clustering. Physical Review Letters, 65, 945-948.
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    • Rose, K.1    Gurewitz, E.2    Fox, G.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.