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 *

101 related articles for article (PubMed ID: 18244407)

  • 1. Using function approximation to analyze the sensitivity of MLP with antisymmetric squashing activation function.
    Yeung DS; Sun X
    IEEE Trans Neural Netw; 2002; 13(1):34-44. PubMed ID: 18244407
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Sensitivity analysis of multilayer perceptron to input and weight perturbations.
    Zeng X; Yeung DS
    IEEE Trans Neural Netw; 2001; 12(6):1358-66. PubMed ID: 18249965
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Sensitivity analysis of multilayer perceptron with differentiable activation functions.
    Choi JY; Choi CH
    IEEE Trans Neural Netw; 1992; 3(1):101-7. PubMed ID: 18276410
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Derivation of the multilayer perceptron weight constraints for direct network interpretation and knowledge discovery.
    Vaughn ML
    Neural Netw; 1999 Nov; 12(9):1259-1271. PubMed ID: 12662631
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A quantified sensitivity measure for multilayer perceptron to input perturbation.
    Zeng X; Yeung DS
    Neural Comput; 2003 Jan; 15(1):183-212. PubMed ID: 12590825
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Computing and analyzing the sensitivity of MLP due to the errors of the i.i.d. inputs and weights based on CLT.
    Yang SS; Ho CL; Siu S
    IEEE Trans Neural Netw; 2010 Dec; 21(12):1882-91. PubMed ID: 20923730
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Implementation of Analog Perceptron as an Essential Element of Configurable Neural Networks.
    Geng C; Sun Q; Nakatake S
    Sensors (Basel); 2020 Jul; 20(15):. PubMed ID: 32751288
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Conventional modeling of the multilayer perceptron using polynomial basis functions.
    Chen MS; Manry MT
    IEEE Trans Neural Netw; 1993; 4(1):164-6. PubMed ID: 18267718
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.
    Yang J; Singh H; Hines EL; Schlaghecken F; Iliescu DD; Leeson MS; Stocks NG
    Artif Intell Med; 2012 Jun; 55(2):117-26. PubMed ID: 22503644
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Geometrical Interpretation and Design of Multilayer Perceptrons.
    Lin R; Zhou Z; You S; Rao R; Kuo CJ
    IEEE Trans Neural Netw Learn Syst; 2024 Feb; 35(2):2545-2559. PubMed ID: 35862331
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Direct explanations for the development and use of a multi-layer perceptron network that classifies low-back-pain patients.
    Vaughn ML; Cavill SJ; Taylor SJ; Foy MA; Fogg AJ
    Int J Neural Syst; 2001 Aug; 11(4):335-47. PubMed ID: 11706409
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multilayer perceptrons: approximation order and necessary number of hidden units.
    Trenn S
    IEEE Trans Neural Netw; 2008 May; 19(5):836-44. PubMed ID: 18467212
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Sleep snoring detection using multi-layer neural networks.
    Nguyen TL; Won Y
    Biomed Mater Eng; 2015; 26 Suppl 1():S1749-55. PubMed ID: 26405943
    [TBL] [Abstract][Full Text] [Related]  

  • 15. On the initialization and optimization of multilayer perceptrons.
    Weymaere N; Martens JP
    IEEE Trans Neural Netw; 1994; 5(5):738-51. PubMed ID: 18267848
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Knowledge-based fuzzy MLP for classification and rule generation.
    Mitra S; De RK; Pal SK
    IEEE Trans Neural Netw; 1997; 8(6):1338-50. PubMed ID: 18255736
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Convolution in Convolution for Network in Network.
    Pang Y; Sun M; Jiang X; Li X
    IEEE Trans Neural Netw Learn Syst; 2018 May; 29(5):1587-1597. PubMed ID: 28328517
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Approximation by fully complex multilayer perceptrons.
    Kim T; Adali T
    Neural Comput; 2003 Jul; 15(7):1641-66. PubMed ID: 12816570
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Enhancing MLP networks using a distributed data representation.
    Narayan S; Tagliarini GA; Page EW
    IEEE Trans Syst Man Cybern B Cybern; 1996; 26(1):143-9. PubMed ID: 18263014
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Performance comparison between Logistic regression, decision trees, and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitus.
    Li CP; Zhi XY; Ma J; Cui Z; Zhu ZL; Zhang C; Hu LP
    Chin Med J (Engl); 2012 Mar; 125(5):851-7. PubMed ID: 22490586
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.