메뉴 건너뛰기




Volumn 65, Issue 1-2, 1997, Pages 1-17

Acceleration of the back propagation through dynamic adaptation of the learning rate

Author keywords

Backpropagation; Convergence; Learning algorithm; Steepest descent

Indexed keywords

ADAPTIVE ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; ITERATIVE METHODS; LEARNING ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION;

EID: 0031346709     PISSN: 00207160     EISSN: None     Source Type: Journal    
DOI: 10.1080/00207167708804597     Document Type: Article
Times cited : (1)

References (16)
  • 1
    • 0001024110 scopus 로고
    • First and Second Order Methods for Learning: Between Steepest Descent and Newton's Method
    • Battiti, R. (1992). First and Second Order Methods for Learning: Between Steepest Descent and Newton's Method, Neural Computation, 4, 141-166.
    • (1992) Neural Computation , vol.4 , pp. 141-166
    • Battiti, R.1
  • 2
    • 0002906163 scopus 로고
    • Improving the convergence of back-propagation learning with second order methods
    • Touretzky, D., Hinton, G. and Sejnowski, T. (Eds.). San Mateo, CA: Morgan kaufmann
    • Becker, S. and Le Cun, Y. (1989). Improving the convergence of back-propagation learning with second order methods. In Touretzky, D., Hinton, G. and Sejnowski, T. (Eds.). Proceedings of the 1988 Connectionist Models Summer School (pp.29-37). San Mateo, CA: Morgan kaufmann.
    • (1989) Proceedings of the 1988 Connectionist Models Summer School , pp. 29-37
    • Becker, S.1    Le Cun, Y.2
  • 3
    • 0023540353 scopus 로고
    • Successfully using Peak Learning Rates of 10 (and greater) in the Back-Propagation Networks with the HEURISTIC Llearning Algorithm
    • CA
    • Carter, J. P. (1987). Successfully using Peak Learning Rates of 10 (and greater) in the Back-Propagation Networks with the HEURISTIC Llearning Algorithm, Proceedings of IEEE, Ist Int. Conf. on NN, San Diego, CA, pp. 645-651.
    • (1987) Proceedings of IEEE, Ist Int. Conf. on NN, San Diego , pp. 645-651
    • Carter, J.P.1
  • 5
    • 0003000735 scopus 로고
    • Faster Learning Varitation on Back Propagation. An Empirical Study
    • Kaufman
    • Fahlman, S.E (1989). Faster Learning Varitation on Back Propagation. An Empirical Study, Proceedings of the 1988 Connectionist Models, Kaufman, pp.38-51.
    • (1989) Proceedings of the 1988 Connectionist Models , pp. 38-51
    • Fahlman, S.E.1
  • 6
    • 0024124741 scopus 로고    scopus 로고
    • Improving the Learning Rate of Back-Propagation with the Gradient Reuse Algorithm
    • san Diego, CA
    • Hush, D. R. and Salas, J. M. Improving the Learning Rate of Back-Propagation with the Gradient Reuse Algorithm, IEEE Int. Conf. on neural Networks, 1, pp. 441-447, san Diego, CA.
    • IEEE Int. Conf. on Neural Networks , vol.1 , pp. 441-447
    • Hush, D.R.1    Salas, J.M.2
  • 7
    • 0024137490 scopus 로고
    • Increased Rates of Convergence through Learning Rate Adaptation
    • Jacobs, R. A. (1988). Increased Rates of Convergence through Learning Rate Adaptation, Neural Networks, 1(4), 295-308.
    • (1988) Neural Networks , vol.1 , Issue.4 , pp. 295-308
    • Jacobs, R.A.1
  • 8
    • 0002290223 scopus 로고
    • Efficient parallel learning algorithms for neural networks
    • Touretzky, D. S. (Ed.), San Mateo, CA: Morgan Kaufmann
    • Kramer, A. H. and Sangiovanni-Vincentelli, A. (1989) Efficient parallel learning algorithms for neural networks, In Touretzky, D. S. (Ed.), Advances in Neural Information systems 1, (pp.40-48). San Mateo, CA: Morgan Kaufmann.
    • (1989) Advances in Neural Information Systems , vol.1 , pp. 40-48
    • Kramer, A.H.1    Sangiovanni-Vincentelli, A.2
  • 9
    • 0003794792 scopus 로고
    • Experiments on Learning Back Propagation
    • Carnegie-Mellon University, Pittusburg, PA
    • Plaut, D. C., Nowlan, S. J. and Hinton, G. E. (1989). Experiments on Learning Back Propagation, Technical Report CMU-CS, 86-126, Carnegie-Mellon University, Pittusburg, PA.
    • (1989) Technical Report CMU-CS, 86-126
    • Plaut, D.C.1    Nowlan, S.J.2    Hinton, G.E.3
  • 15
    • 0028292832 scopus 로고
    • Minimisation Methods for Training Feedforward Neural Networks
    • Van Der Smagt, P. P. (1994). Minimisation Methods for Training Feedforward Neural Networks, Neural Networks, 7(1), pp.1-11.
    • (1994) Neural Networks , vol.7 , Issue.1 , pp. 1-11
    • Van Der Smagt, P.P.1
  • 16
    • 0023541050 scopus 로고
    • Learning Algorithms for Connectionist Networks:Applied Gradient Methods of Nonlinear Optimization
    • San Diego, CA
    • Watrous, R. L. (1987). Learning Algorithms for Connectionist Networks:Applied Gradient Methods of Nonlinear Optimization, Proceedings of the IEEE 1st International Conference on Neural Networks, San Diego, CA, pp. 619-627.
    • (1987) Proceedings of the IEEE 1st International Conference on Neural Networks , pp. 619-627
    • Watrous, R.L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.