BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

85 related articles for article (PubMed ID: 24808529)

  • 1. Structure of indicator function classes with finite Vapnik-Chervonenkis dimensions.
    Zhang C; Tao D
    IEEE Trans Neural Netw Learn Syst; 2013 Jul; 24(7):1156-60. PubMed ID: 24808529
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Discretized-Vapnik-Chervonenkis dimension for analyzing complexity of real function classes.
    Zhang C; Bian W; Tao D; Lin W
    IEEE Trans Neural Netw Learn Syst; 2012 Sep; 23(9):1461-72. PubMed ID: 24807929
    [TBL] [Abstract][Full Text] [Related]  

  • 3. The Vapnik-Chervonenkis dimension of graph and recursive neural networks.
    Scarselli F; Tsoi AC; Hagenbuchner M
    Neural Netw; 2018 Dec; 108():248-259. PubMed ID: 30219742
    [TBL] [Abstract][Full Text] [Related]  

  • 4. On the complexity of computing and learning with multiplicative neural networks.
    Schmitt M
    Neural Comput; 2002 Feb; 14(2):241-301. PubMed ID: 11802913
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. VC-dimension of univariate decision trees.
    Yildiz OT
    IEEE Trans Neural Netw Learn Syst; 2015 Feb; 26(2):378-87. PubMed ID: 25594983
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A local Vapnik-Chervonenkis complexity.
    Oneto L; Anguita D; Ridella S
    Neural Netw; 2016 Oct; 82():62-75. PubMed ID: 27474843
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Evaluating the Vapnik-Chervonenkis dimension of artificial neural networks using the PoincarĂ© polynomial.
    Carter MA; Oxley ME
    Neural Netw; 1999 Apr; 12(3):403-408. PubMed ID: 12662683
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Model complexity control for regression using VC generalization bounds.
    Cherkassky V; Shao X; Mulier FM; Vapnik VN
    IEEE Trans Neural Netw; 1999; 10(5):1075-89. PubMed ID: 18252610
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The VC dimension for mixtures of binary classifiers.
    Jiang W
    Neural Comput; 2000 Jun; 12(6):1293-301. PubMed ID: 10935713
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Neurodynamical classifiers with low model complexity.
    Pant H; Soman S; Jayadeva ; Bhaya A
    Neural Netw; 2020 Dec; 132():405-415. PubMed ID: 33011671
    [TBL] [Abstract][Full Text] [Related]  

  • 12. On the capabilities of higher-order neurons: a radial basis function approach.
    Schmitt M
    Neural Comput; 2005 Mar; 17(3):715-29. PubMed ID: 15802012
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Descartes' rule of signs for radial basis function neural networks.
    Schmitt M
    Neural Comput; 2002 Dec; 14(12):2997-3011. PubMed ID: 12487802
    [TBL] [Abstract][Full Text] [Related]  

  • 14. VC-dimension of exterior visibility.
    Isler V; Kannan S; Daniilidis K; Valtr P
    IEEE Trans Pattern Anal Mach Intell; 2004 May; 26(5):667-71. PubMed ID: 15460289
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Measuring the VC-dimension using optimized experimental design.
    Shao X; Cherkassky V; Li W
    Neural Comput; 2000 Aug; 12(8):1969-86. PubMed ID: 10953247
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A comparative analysis of support vector machines and extreme learning machines.
    Liu X; Gao C; Li P
    Neural Netw; 2012 Sep; 33():58-66. PubMed ID: 22572469
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Bounds on the number of hidden neurons in three-layer binary neural networks.
    Zhang Z; Ma X; Yang Y
    Neural Netw; 2003 Sep; 16(7):995-1002. PubMed ID: 14692634
    [TBL] [Abstract][Full Text] [Related]  

  • 18. On the practical applicability of VC dimension bounds.
    Holden SB; Niranjan M
    Neural Comput; 1995 Nov; 7(6):1265-88. PubMed ID: 7584902
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A deep connection between the Vapnik-Chervonenkis entropy and the Rademacher complexity.
    Anguita D; Ghio A; Oneto L; Ridella S
    IEEE Trans Neural Netw Learn Syst; 2014 Dec; 25(12):2202-11. PubMed ID: 25420243
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Signal estimation and denoising using VC-theory.
    Cherkassky V; Shao X
    Neural Netw; 2001 Jan; 14(1):37-52. PubMed ID: 11213212
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 5.