BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

295 related articles for article (PubMed ID: 16566479)

  • 1. Convergence of gradient method with momentum for two-layer feedforward neural networks.
    Zhang N; Wu W; Zheng G
    IEEE Trans Neural Netw; 2006 Mar; 17(2):522-5. PubMed ID: 16566479
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Convergence of cyclic and almost-cyclic learning with momentum for feedforward neural networks.
    Wang J; Yang J; Wu W
    IEEE Trans Neural Netw; 2011 Aug; 22(8):1297-306. PubMed ID: 21813357
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Magnified gradient function with deterministic weight modification in adaptive learning.
    Ng SC; Cheung CC; Leung SH
    IEEE Trans Neural Netw; 2004 Nov; 15(6):1411-23. PubMed ID: 15565769
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Implementing online natural gradient learning: problems and solutions.
    Wan W
    IEEE Trans Neural Netw; 2006 Mar; 17(2):317-29. PubMed ID: 16566461
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Boundedness and convergence of online gradient method with penalty for feedforward neural networks.
    Zhang H; Wu W; Liu F; Yao M
    IEEE Trans Neural Netw; 2009 Jun; 20(6):1050-4. PubMed ID: 19435681
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Incremental communication for adaptive resonance theory networks.
    Chen M; Ghorbani AA; Bhavsar VC
    IEEE Trans Neural Netw; 2005 Jan; 16(1):132-44. PubMed ID: 15732394
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Toward the training of feed-forward neural networks with the D-optimum input sequence.
    Witczak M
    IEEE Trans Neural Netw; 2006 Mar; 17(2):357-73. PubMed ID: 16566464
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Convergence analysis of a deterministic discrete time system of Oja's PCA learning algorithm.
    Yi Z; Ye M; Lv JC; Tan KK
    IEEE Trans Neural Netw; 2005 Nov; 16(6):1318-28. PubMed ID: 16342477
    [TBL] [Abstract][Full Text] [Related]  

  • 9. On adaptive learning rate that guarantees convergence in feedforward networks.
    Behera L; Kumar S; Patnaik A
    IEEE Trans Neural Netw; 2006 Sep; 17(5):1116-25. PubMed ID: 17001974
    [TBL] [Abstract][Full Text] [Related]  

  • 10. On the new method for the control of discrete nonlinear dynamic systems using neural networks.
    Deng H; Li HX
    IEEE Trans Neural Netw; 2006 Mar; 17(2):526-9. PubMed ID: 16566480
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Computation of Adalines' sensitivity to weight perturbation.
    Zeng X; Wang Y; Zhang K
    IEEE Trans Neural Netw; 2006 Mar; 17(2):515-9. PubMed ID: 16566477
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The linear separability problem: some testing methods.
    Elizondo D
    IEEE Trans Neural Netw; 2006 Mar; 17(2):330-44. PubMed ID: 16566462
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The parameterless self-organizing map algorithm.
    Berglund E; Sitte J
    IEEE Trans Neural Netw; 2006 Mar; 17(2):305-16. PubMed ID: 16566460
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Batch map extensions of the kernel-based maximum entropy learning rule.
    Gautama T; Van Hulle MM
    IEEE Trans Neural Netw; 2006 Mar; 17(2):529-32. PubMed ID: 16566481
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deterministic convergence of an online gradient method for BP neural networks.
    Wu W; Feng G; Li Z; Xu Y
    IEEE Trans Neural Netw; 2005 May; 16(3):533-40. PubMed ID: 15940984
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Global convergence of online BP training with dynamic learning rate.
    Zhang R; Xu ZB; Huang GB; Wang D
    IEEE Trans Neural Netw Learn Syst; 2012 Feb; 23(2):330-41. PubMed ID: 24808511
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Practical training framework for fitting a function and its derivatives.
    Pukrittayakamee A; Hagan M; Raff L; Bukkapatnam ST; Komanduri R
    IEEE Trans Neural Netw; 2011 Jun; 22(6):936-47. PubMed ID: 21592919
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deterministic learning for maximum-likelihood estimation through neural networks.
    Cervellera C; Macciò D; Muselli M
    IEEE Trans Neural Netw; 2008 Aug; 19(8):1456-67. PubMed ID: 18701374
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Partially connected feedforward neural networks structured by input types.
    Kang S; Isik C
    IEEE Trans Neural Netw; 2005 Jan; 16(1):175-84. PubMed ID: 15732397
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Convergence analysis of a simple minor component analysis algorithm.
    Peng D; Yi Z; Luo W
    Neural Netw; 2007 Sep; 20(7):842-50. PubMed ID: 17765471
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.