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 *

84 related articles for article (PubMed ID: 18276358)

  • 1. A norm selection criterion for the generalized delta rule.
    Burrascano P
    IEEE Trans Neural Netw; 1991; 2(1):125-30. PubMed ID: 18276358
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Novel maximum-margin training algorithms for supervised neural networks.
    Ludwig O; Nunes U
    IEEE Trans Neural Netw; 2010 Jun; 21(6):972-84. PubMed ID: 20409990
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A fast feedforward training algorithm using a modified form of the standard backpropagation algorithm.
    Abid S; Fnaiech F; Najim M
    IEEE Trans Neural Netw; 2001; 12(2):424-30. PubMed ID: 18244397
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Accelerating the training of feedforward neural networks using generalized Hebbian rules for initializing the internal representations.
    Karayiannis NB
    IEEE Trans Neural Netw; 1996; 7(2):419-26. PubMed ID: 18255595
    [TBL] [Abstract][Full Text] [Related]  

  • 5. FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling.
    Back AD; Tsoi AC
    Neural Comput; 1991; 3(3):375-385. PubMed ID: 31167315
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The Q-norm complexity measure and the minimum gradient method: a novel approach to the machine learning structural risk minimization problem.
    Vieira DA; Takahashi RH; Palade V; Vasconcelos JA; Caminhas WM
    IEEE Trans Neural Netw; 2008 Aug; 19(8):1415-30. PubMed ID: 18701371
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Deterministic global optimization for FNN training.
    Toh KA
    IEEE Trans Syst Man Cybern B Cybern; 2003; 33(6):977-83. PubMed ID: 18238248
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors.
    Tsuda K; Sugiyama M; Miller KR
    IEEE Trans Neural Netw; 2002; 13(1):70-80. PubMed ID: 18244410
    [TBL] [Abstract][Full Text] [Related]  

  • 10. POPFNN-CRI(S): pseudo outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier.
    Ang KK; Quek C; Pasquier M
    IEEE Trans Syst Man Cybern B Cybern; 2003; 33(6):838-49. PubMed ID: 18238237
    [TBL] [Abstract][Full Text] [Related]  

  • 11. MLPNN Training via a Multiobjective Optimization of Training Error and Stochastic Sensitivity.
    Yeung DS; Li JC; Ng WW; Chan PP
    IEEE Trans Neural Netw Learn Syst; 2016 May; 27(5):978-92. PubMed ID: 26054075
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Subspace information criterion for model selection.
    Sugiyama M; Ogawa H
    Neural Comput; 2001 Aug; 13(8):1863-89. PubMed ID: 11506674
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Capabilities of a four-layered feedforward neural network: four layers versus three.
    Tamura S; Tateishi M
    IEEE Trans Neural Netw; 1997; 8(2):251-5. PubMed ID: 18255629
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Generalized information potential criterion for adaptive system training.
    Erdogmus D; Principe JC
    IEEE Trans Neural Netw; 2002; 13(5):1035-44. PubMed ID: 18244501
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Sensitivity-based adaptive learning rules for binary feedforward neural networks.
    Zhong S; Zeng X; Wu S; Han L
    IEEE Trans Neural Netw Learn Syst; 2012 Mar; 23(3):480-91. PubMed ID: 24808553
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Performance analysis of a pipelined backpropagation parallel algorithm.
    Petrowski A; Dreyfus G; Girault C
    IEEE Trans Neural Netw; 1993; 4(6):970-81. PubMed ID: 18276527
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Neural discriminant analysis.
    Tsujitani M; Koshimizu T
    IEEE Trans Neural Netw; 2000; 11(6):1394-401. PubMed ID: 18249863
    [TBL] [Abstract][Full Text] [Related]  

  • 18. ANN-DT: an algorithm for extraction of decision trees from artificial neural networks.
    Schmitz GJ; Aldrich C; Gouws FS
    IEEE Trans Neural Netw; 1999; 10(6):1392-401. PubMed ID: 18252640
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Convergent decomposition techniques for training RBF neural networks.
    Buzzi C; Grippo L; Sciandrone M
    Neural Comput; 2001 Aug; 13(8):1891-920. PubMed ID: 11506675
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms.
    Karayiannis NB; Randolph-Gips MM
    IEEE Trans Neural Netw; 2005 Mar; 16(2):423-35. PubMed ID: 15787149
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 5.