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 *

302 related articles for article (PubMed ID: 20417323)

  • 1. A tutorial on support vector machine-based methods for classification problems in chemometrics.
    Luts J; Ojeda F; Van de Plas R; De Moor B; Van Huffel S; Suykens JA
    Anal Chim Acta; 2010 Apr; 665(2):129-45. PubMed ID: 20417323
    [TBL] [Abstract][Full Text] [Related]  

  • 2. New support vector-based design method for binary hierarchical classifiers for multi-class classification problems.
    Wang YC; Casasent D
    Neural Netw; 2008; 21(2-3):502-10. PubMed ID: 18187285
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A support vector machine using the lazy learning approach for multi-class classification.
    Comak E; Arslan A
    J Med Eng Technol; 2006; 30(2):73-7. PubMed ID: 16531345
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Between classification-error approximation and weighted least-squares learning.
    Toh KA; Eng HL
    IEEE Trans Pattern Anal Mach Intell; 2008 Apr; 30(4):658-69. PubMed ID: 18276971
    [TBL] [Abstract][Full Text] [Related]  

  • 5. An overview and performance evaluation of classification-based least squares trained filters.
    Shao L; Zhang H; de Haan G
    IEEE Trans Image Process; 2008 Oct; 17(10):1772-82. PubMed ID: 18784026
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion.
    Rahman MM; Desai BC; Bhattacharya P
    Comput Med Imaging Graph; 2008 Mar; 32(2):95-108. PubMed ID: 18037271
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A novel kernel-based maximum a posteriori classification method.
    Xu Z; Huang K; Zhu J; King I; Lyu MR
    Neural Netw; 2009 Sep; 22(7):977-87. PubMed ID: 19167865
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns.
    De Martino F; Valente G; Staeren N; Ashburner J; Goebel R; Formisano E
    Neuroimage; 2008 Oct; 43(1):44-58. PubMed ID: 18672070
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A practical approach to model selection for support vector machines with a Gaussian kernel.
    Varewyck M; Martens JP
    IEEE Trans Syst Man Cybern B Cybern; 2011 Apr; 41(2):330-40. PubMed ID: 20699214
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Terminated Ramp-Support vector machines: a nonparametric data dependent kernel.
    Merler S; Jurman G
    Neural Netw; 2006 Dec; 19(10):1597-611. PubMed ID: 16603338
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Feature selection with kernel class separability.
    Wang L
    IEEE Trans Pattern Anal Mach Intell; 2008 Sep; 30(9):1534-46. PubMed ID: 18617713
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Real-time epileptic seizure prediction using AR models and support vector machines.
    Chisci L; Mavino A; Perferi G; Sciandrone M; Anile C; Colicchio G; Fuggetta F
    IEEE Trans Biomed Eng; 2010 May; 57(5):1124-32. PubMed ID: 20172805
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Customizing kernel functions for SVM-based hyperspectral image classification.
    Guo B; Gunn SR; Damper RI; Nelson JB
    IEEE Trans Image Process; 2008 Apr; 17(4):622-9. PubMed ID: 18390369
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Domain adaptation problems: a DASVM classification technique and a circular validation strategy.
    Bruzzone L; Marconcini M
    IEEE Trans Pattern Anal Mach Intell; 2010 May; 32(5):770-87. PubMed ID: 20299704
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines.
    Cawley GC; Talbot NL
    Neural Netw; 2004 Dec; 17(10):1467-75. PubMed ID: 15541948
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Two criteria for model selection in multiclass support vector machines.
    Wang L; Xue P; Chan KL
    IEEE Trans Syst Man Cybern B Cybern; 2008 Dec; 38(6):1432-48. PubMed ID: 19022717
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis.
    Van Gestel T; Suykens JA; Lanckriet G; Lambrechts A; De Moor B; Vandewalle J
    Neural Comput; 2002 May; 14(5):1115-47. PubMed ID: 11972910
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Wavelet frame accelerated reduced support vector machines.
    Ratsch M; Teschke G; Romdhani S; Vetter T
    IEEE Trans Image Process; 2008 Dec; 17(12):2456-64. PubMed ID: 19004715
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Handling missing values in support vector machine classifiers.
    Pelckmans K; De Brabanter J; Suykens JA; De Moor B
    Neural Netw; 2005; 18(5-6):684-92. PubMed ID: 16111866
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Gabor-based kernel PCA with fractional power polynomial models for face recognition.
    Liu C
    IEEE Trans Pattern Anal Mach Intell; 2004 May; 26(5):572-81. PubMed ID: 15460279
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.