Mean field approach to bayes learning in feed-forward neural networks

被引:0
|
作者
机构
来源
Phys Rev Lett | / 11卷 / 1964期
关键词
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 50 条
  • [1] Mean field approach to bayes learning in feed-forward neural networks
    Opper, M
    Winther, O
    PHYSICAL REVIEW LETTERS, 1996, 76 (11) : 1964 - 1967
  • [2] A mean field algorithm for Bayes learning in large feed-forward neural networks
    Opper, M
    Winther, O
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 9: PROCEEDINGS OF THE 1996 CONFERENCE, 1997, 9 : 225 - 231
  • [3] Feed-forward neural networks
    Bebis, George
    Georgiopoulos, Michael
    IEEE Potentials, 1994, 13 (04): : 27 - 31
  • [4] Field Programmable Neural Array for Feed-Forward Neural Networks
    Bohrn, Marek
    Fujcik, Lukas
    Vrba, Radimir
    2013 36TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), 2013, : 727 - 731
  • [5] Optimizing and Learning Algorithm for Feed-forward Neural Networks
    Bachiller, Pilar
    González, Julia
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2001, 5 (01) : 51 - 57
  • [6] Categorization and effective perceptron learning in feed-forward neural networks
    Waelbroeck, H
    Zertuche, F
    JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 2000, 33 (33): : 5809 - 5818
  • [7] Evolutionary approach to training feed-forward and recurrent neural networks
    Riley, Jeff
    Ciesielski, Victor B.
    International Conference on Knowledge-Based Intelligent Electronic Systems, Proceedings, KES, 1998, 3 : 596 - 602
  • [8] Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks
    Aguiar, Manuela A. D.
    Dias, Ana Paula S.
    Ferreira, Flora
    CHAOS, 2017, 27 (01)
  • [9] A quantum model of feed-forward neural networks with unitary learning algorithms
    Changpeng Shao
    Quantum Information Processing, 2020, 19
  • [10] Hybrid learning schemes for fast training of feed-forward neural networks
    Karayiannis, NB
    MATHEMATICS AND COMPUTERS IN SIMULATION, 1996, 41 (1-2) : 13 - 28