BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

173 related articles for article (PubMed ID: 12662846)

  • 1. Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results.
    Chung Tsoi A; Scarselli F
    Neural Netw; 1998 Jan; 11(1):15-37. PubMed ID: 12662846
    [TBL] [Abstract][Full Text] [Related]  

  • 2. On the approximation by single hidden layer feedforward neural networks with fixed weights.
    Guliyev NJ; Ismailov VE
    Neural Netw; 2018 Feb; 98():296-304. PubMed ID: 29301110
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Existence and uniqueness results for neural network approximations.
    Williamson RC; Helmke U
    IEEE Trans Neural Netw; 1995; 6(1):2-13. PubMed ID: 18263280
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Local coupled feedforward neural network.
    Sun J
    Neural Netw; 2010 Jan; 23(1):108-13. PubMed ID: 19596550
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function.
    Guliyev NJ; Ismailov VE
    Neural Comput; 2016 Jul; 28(7):1289-304. PubMed ID: 27171269
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. An improvement of extreme learning machine for compact single-hidden-layer feedforward neural networks.
    Huynh HT; Won Y; Kim JJ
    Int J Neural Syst; 2008 Oct; 18(5):433-41. PubMed ID: 18991365
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation.
    Wang J; Deng Z; Luo X; Jiang Y; Wang S
    Neural Netw; 2016 Jun; 78():51-64. PubMed ID: 27049545
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Constructive algorithms for structure learning in feedforward neural networks for regression problems.
    Kwok TY; Yeung DY
    IEEE Trans Neural Netw; 1997; 8(3):630-45. PubMed ID: 18255666
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Universal approximation using incremental constructive feedforward networks with random hidden nodes.
    Huang GB; Chen L; Siew CK
    IEEE Trans Neural Netw; 2006 Jul; 17(4):879-892. PubMed ID: 16856652
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A new formulation for feedforward neural networks.
    Razavi S; Tolson BA
    IEEE Trans Neural Netw; 2011 Oct; 22(10):1588-98. PubMed ID: 21859600
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The local minima-free condition of feedforward neural networks for outer-supervised learning.
    Huang DS
    IEEE Trans Syst Man Cybern B Cybern; 1998; 28(3):477-80. PubMed ID: 18255966
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A new constructive algorithm for architectural and functional adaptation of artificial neural networks.
    Islam MM; Sattar MA; Amin MF; Yao X; Murase K
    IEEE Trans Syst Man Cybern B Cybern; 2009 Dec; 39(6):1590-605. PubMed ID: 19502131
    [TBL] [Abstract][Full Text] [Related]  

  • 14. On the classification capability of a dynamic threshold neural network.
    Chiang CC; Fu HC
    Int J Neural Syst; 1994 Jun; 5(2):103-14. PubMed ID: 7812498
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An effective SteinGLM initialization scheme for training multi-layer feedforward sigmoidal neural networks.
    Yang Z; Zhang H; Sudjianto A; Zhang A
    Neural Netw; 2021 Jul; 139():149-157. PubMed ID: 33706228
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The layer-wise method and the backpropagation hybrid approach to learning a feedforward neural network.
    Rubanov NS
    IEEE Trans Neural Netw; 2000; 11(2):295-305. PubMed ID: 18249761
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Learning algorithms for feedforward networks based on finite samples.
    Rao NV; Protopopescu V; Mann RC; Oblow EM; Iyengar SS
    IEEE Trans Neural Netw; 1996; 7(4):926-40. PubMed ID: 18263488
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A pruning feedforward small-world neural network based on Katz centrality for nonlinear system modeling.
    Li W; Chu M; Qiao J
    Neural Netw; 2020 Oct; 130():269-285. PubMed ID: 32711349
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An orthogonal neural network for function approximation.
    Yang SS; Tseng CS
    IEEE Trans Syst Man Cybern B Cybern; 1996; 26(5):779-85. PubMed ID: 18263076
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Learning capability and storage capacity of two-hidden-layer feedforward networks.
    Huang GB
    IEEE Trans Neural Netw; 2003; 14(2):274-81. PubMed ID: 18238011
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.