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 *

79 related articles for article (PubMed ID: 12079546)

  • 1. Kernel-based topographic map formation by local density modeling.
    Van Hulle MM
    Neural Comput; 2002 Jul; 14(7):1561-73. PubMed ID: 12079546
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Joint entropy maximization in kernel-based topographic maps.
    Van Hulle MM
    Neural Comput; 2002 Aug; 14(8):1887-906. PubMed ID: 12180406
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Topographic map formation of factorized Edgeworth-expanded kernels.
    Van Hulle MM
    Neural Netw; 2006; 19(6-7):744-50. PubMed ID: 16759836
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Maximum likelihood topographic map formation.
    Van Hulle MM
    Neural Comput; 2005 Mar; 17(3):503-13. PubMed ID: 15802004
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Entropy-based kernel mixture modeling for topographic map formation.
    Van Hulle MM
    IEEE Trans Neural Netw; 2004 Jul; 15(4):850-8. PubMed ID: 15461078
    [TBL] [Abstract][Full Text] [Related]  

  • 6. On a class of support vector kernels based on frames in function Hilbert spaces.
    Gao JB; Harris CJ; Gunn SR
    Neural Comput; 2001 Sep; 13(9):1975-94. PubMed ID: 11516353
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Synaptic weight normalization effects for topographic mapping formation.
    Sakamoto S
    Neural Netw; 2004; 17(8-9):1109-20. PubMed ID: 15555855
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Gradient-based adaptation of general gaussian kernels.
    Glasmachers T; Igel C
    Neural Comput; 2005 Oct; 17(10):2099-105. PubMed ID: 16105219
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Stability of generalized topographic mappings between cell layers through correlational learning.
    Sakamoto S; Seki S; Kobuchi Y
    Neural Netw; 2004; 17(8-9):1101-7. PubMed ID: 15555854
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Gaussian mean-shift is an EM algorithm.
    Carreira-Perpiñán MA
    IEEE Trans Pattern Anal Mach Intell; 2007 May; 29(5):767-76. PubMed ID: 17356198
    [TBL] [Abstract][Full Text] [Related]  

  • 11. On the equivalence between kernel self-organising maps and self-organising mixture density networks.
    Yin H
    Neural Netw; 2006; 19(6-7):780-4. PubMed ID: 16759835
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Missing data imputation through GTM as a mixture of t-distributions.
    Vellido A
    Neural Netw; 2006 Dec; 19(10):1624-35. PubMed ID: 16580176
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Robust L1 principal component analysis and its Bayesian variational inference.
    Gao J
    Neural Comput; 2008 Feb; 20(2):555-72. PubMed ID: 18045015
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Learning Gaussian mixture models with entropy-based criteria.
    Penalver Benavent A; Escolano Ruiz F; Saez JM
    IEEE Trans Neural Netw; 2009 Nov; 20(11):1756-71. PubMed ID: 19770090
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The time-organized map algorithm: extending the self-organizing map to spatiotemporal signals.
    Wiemer JC
    Neural Comput; 2003 May; 15(5):1143-71. PubMed ID: 12803960
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Edgeworth-expanded gaussian mixture density modeling.
    Van Hulle MM
    Neural Comput; 2005 Aug; 17(8):1706-14. PubMed ID: 16041866
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Rademacher chaos complexities for learning the kernel problem.
    Ying Y; Campbell C
    Neural Comput; 2010 Nov; 22(11):2858-86. PubMed ID: 20804384
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Mapping the dimensionality, density and topology of data: the growing adaptive neural gas.
    Cselényi Z
    Comput Methods Programs Biomed; 2005 May; 78(2):141-56. PubMed ID: 15848269
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: a case study on renal function evaluation.
    Song Q; Kasabov N; Ma T; Marshall MR
    Artif Intell Med; 2006 Mar; 36(3):235-44. PubMed ID: 16213694
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Sequential kernel density approximation and its application to real-time visual tracking.
    Han B; Comaniciu D; Zhu Y; Davis LS
    IEEE Trans Pattern Anal Mach Intell; 2008 Jul; 30(7):1186-97. PubMed ID: 18550902
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 4.