BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

476 related articles for article (PubMed ID: 19596632)

  • 1. Error minimized extreme learning machine with growth of hidden nodes and incremental learning.
    Feng G; Huang GB; Lin Q; Gay R
    IEEE Trans Neural Netw; 2009 Aug; 20(8):1352-7. PubMed ID: 19596632
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Universal approximation of extreme learning machine with adaptive growth of hidden nodes.
    Zhang R; Lan Y; Huang GB; Xu ZB
    IEEE Trans Neural Netw Learn Syst; 2012 Feb; 23(2):365-71. PubMed ID: 24808516
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Bidirectional extreme learning machine for regression problem and its learning effectiveness.
    Yang Y; Wang Y; Yuan X
    IEEE Trans Neural Netw Learn Syst; 2012 Sep; 23(9):1498-505. PubMed ID: 24807932
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Dynamic extreme learning machine and its approximation capability.
    Zhang R; Lan Y; Huang GB; Xu ZB; Soh YC
    IEEE Trans Cybern; 2013 Dec; 43(6):2054-65. PubMed ID: 23757515
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A fast and accurate online sequential learning algorithm for feedforward networks.
    Liang NY; Huang GB; Saratchandran P; Sundararajan N
    IEEE Trans Neural Netw; 2006 Nov; 17(6):1411-23. PubMed ID: 17131657
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Sparse Bayesian extreme learning machine for multi-classification.
    Luo J; Vong CM; Wong PK
    IEEE Trans Neural Netw Learn Syst; 2014 Apr; 25(4):836-43. PubMed ID: 24807961
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks.
    Romero E; Alquézar R
    Neural Netw; 2012 Jan; 25(1):122-9. PubMed ID: 21959130
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A novel multiple instance learning method based on extreme learning machine.
    Wang J; Cai L; Peng J; Jia Y
    Comput Intell Neurosci; 2015; 2015():405890. PubMed ID: 25705220
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Is extreme learning machine feasible? A theoretical assessment (part I).
    Liu X; Lin S; Fang J; Xu Z
    IEEE Trans Neural Netw Learn Syst; 2015 Jan; 26(1):7-20. PubMed ID: 25069126
    [TBL] [Abstract][Full Text] [Related]  

  • 11. BELM: Bayesian extreme learning machine.
    Soria-Olivas E; Gómez-Sanchis J; Martín JD; Vila-Francés J; Martínez M; Magdalena JR; Serrano AJ
    IEEE Trans Neural Netw; 2011 Mar; 22(3):505-9. PubMed ID: 21257373
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Constructive approximation to multivariate function by decay RBF neural network.
    Hou M; Han X
    IEEE Trans Neural Netw; 2010 Sep; 21(9):1517-23. PubMed ID: 20693108
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A new Jacobian matrix for optimal learning of single-layer neural networks.
    Peng JX; Li K; Irwin GW
    IEEE Trans Neural Netw; 2008 Jan; 19(1):119-29. PubMed ID: 18269943
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Extreme learning machine for regression and multiclass classification.
    Huang GB; Zhou H; Ding X; Zhang R
    IEEE Trans Syst Man Cybern B Cybern; 2012 Apr; 42(2):513-29. PubMed ID: 21984515
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Single-hidden-layer feed-forward quantum neural network based on Grover learning.
    Liu CY; Chen C; Chang CT; Shih LM
    Neural Netw; 2013 Sep; 45():144-50. PubMed ID: 23545155
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Is extreme learning machine feasible? A theoretical assessment (part II).
    Lin S; Liu X; Fang J; Xu Z
    IEEE Trans Neural Netw Learn Syst; 2015 Jan; 26(1):21-34. PubMed ID: 25069128
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function.
    Yap KS; Lim CP; Abidin IZ
    IEEE Trans Neural Netw; 2008 Sep; 19(9):1641-6. PubMed ID: 18779094
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Neural architecture design based on extreme learning machine.
    Bueno-Crespo A; García-Laencina PJ; Sancho-Gómez JL
    Neural Netw; 2013 Dec; 48():19-24. PubMed ID: 23892908
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Fuzzy associative conjuncted maps network.
    Goh H; Lim JH; Quek C
    IEEE Trans Neural Netw; 2009 Aug; 20(8):1302-19. PubMed ID: 19635694
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 24.