These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

164 related articles for article (PubMed ID: 12662846)

  • 41. Neural subnet design by direct polynomial mapping.
    Rohani K; Chen MS; Manry MT
    IEEE Trans Neural Netw; 1992; 3(6):1024-6. PubMed ID: 18276501
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm.
    Tsai JT; Chou JH; Liu TK
    IEEE Trans Neural Netw; 2006 Jan; 17(1):69-80. PubMed ID: 16526477
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection.
    Belciug S; Gorunescu F
    J Biomed Inform; 2018 Jul; 83():159-166. PubMed ID: 29890313
    [TBL] [Abstract][Full Text] [Related]  

  • 44. Feedforward neural network construction using cross validation.
    Setiono R
    Neural Comput; 2001 Dec; 13(12):2865-77. PubMed ID: 11705414
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Constructive training methods for feedforward neural networks with binary weights.
    Mayoraz E; Aviolat F
    Int J Neural Syst; 1996 May; 7(2):149-66. PubMed ID: 8823625
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Efficient self-organizing multilayer neural network for nonlinear system modeling.
    Han HG; Wang LD; Qiao JF
    Neural Netw; 2013 Jul; 43():22-32. PubMed ID: 23500497
    [TBL] [Abstract][Full Text] [Related]  

  • 47. A learning rule for very simple universal approximators consisting of a single layer of perceptrons.
    Auer P; Burgsteiner H; Maass W
    Neural Netw; 2008 Jun; 21(5):786-95. PubMed ID: 18249524
    [TBL] [Abstract][Full Text] [Related]  

  • 48. The Chebyshev-polynomials-based unified model neural networks for function approximation.
    Lee TT; Jeng JT
    IEEE Trans Syst Man Cybern B Cybern; 1998; 28(6):925-35. PubMed ID: 18256014
    [TBL] [Abstract][Full Text] [Related]  

  • 49. On sequential construction of binary neural networks.
    Muselli M
    IEEE Trans Neural Netw; 1995; 6(3):678-90. PubMed ID: 18263353
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Extracting rules from neural networks by pruning and hidden-unit splitting.
    Setiono R
    Neural Comput; 1997 Jan; 9(1):205-25. PubMed ID: 9117899
    [TBL] [Abstract][Full Text] [Related]  

  • 51. New learning automata based algorithms for adaptation of backpropagation algorithm parameters.
    Meybodi MR; Beigy H
    Int J Neural Syst; 2002 Feb; 12(1):45-67. PubMed ID: 11852444
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Fast sigmoidal networks via spiking neurons.
    Maass W
    Neural Comput; 1997 Feb; 9(2):279-304. PubMed ID: 9117904
    [TBL] [Abstract][Full Text] [Related]  

  • 53. A systematic and effective supervised learning mechanism based on Jacobian rank deficiency.
    Zhou G; Si J
    Neural Comput; 1998 May; 10(4):1031-45. PubMed ID: 9573418
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Neural networks with local receptive fields and superlinear VC dimension.
    Schmitt M
    Neural Comput; 2002 Apr; 14(4):919-56. PubMed ID: 11936967
    [TBL] [Abstract][Full Text] [Related]  

  • 55. New training strategies for constructive neural networks with application to regression problems.
    Ma L; Khorasani K
    Neural Netw; 2004 May; 17(4):589-609. PubMed ID: 15109686
    [TBL] [Abstract][Full Text] [Related]  

  • 56. GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework.
    Deng L; Jiao P; Pei J; Wu Z; Li G
    Neural Netw; 2018 Apr; 100():49-58. PubMed ID: 29471195
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Estimating the number of hidden neurons in a feedforward network using the singular value decomposition.
    Teoh EJ; Tan KC; Xiang C
    IEEE Trans Neural Netw; 2006 Nov; 17(6):1623-9. PubMed ID: 17131674
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting.
    Coop R; Mishtal A; Arel I
    IEEE Trans Neural Netw Learn Syst; 2013 Oct; 24(10):1623-34. PubMed ID: 24808599
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions.
    Huang GB; Babri HA
    IEEE Trans Neural Netw; 1998; 9(1):224-9. PubMed ID: 18252445
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Multilayer neural networks and Bayes decision theory.
    Funahashi K
    Neural Netw; 1998 Mar; 11(2):209-213. PubMed ID: 12662832
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.