179 related articles for article (PubMed ID: 27723811)
1. Explaining Support Vector Machines: A Color Based Nomogram.
Van Belle V; Van Calster B; Van Huffel S; Suykens JA; Lisboa P
PLoS One; 2016; 11(10):e0164568. PubMed ID: 27723811
[TBL] [Abstract][Full Text] [Related]
2. White box radial basis function classifiers with component selection for clinical prediction models.
Van Belle V; Lisboa P
Artif Intell Med; 2014 Jan; 60(1):53-64. PubMed ID: 24262978
[TBL] [Abstract][Full Text] [Related]
3. Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels.
Cho BH; Yu H; Lee J; Chee YJ; Kim IY; Kim SI
IEEE Trans Inf Technol Biomed; 2008 Mar; 12(2):247-56. PubMed ID: 18348954
[TBL] [Abstract][Full Text] [Related]
4. Evaluation of a Quasi-fractal Dimension to Enhance Breast Cancer Detection in X-ray Mammograms using Support Vector Machine.
Murase K; Tanki N; Miyazaki S; Nagao M
Igaku Butsuri; 2008; 28(1):15-25. PubMed ID: 21976251
[TBL] [Abstract][Full Text] [Related]
5. A multi-label learning based kernel automatic recommendation method for support vector machine.
Zhang X; Song Q
PLoS One; 2015; 10(3):e0120455. PubMed ID: 25893896
[TBL] [Abstract][Full Text] [Related]
6. Efficient classification for additive kernel SVMs.
Maji S; Berg AC; Malik J
IEEE Trans Pattern Anal Mach Intell; 2013 Jan; 35(1):66-77. PubMed ID: 22392703
[TBL] [Abstract][Full Text] [Related]
7. Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study.
Lima CA; Coelho AL
Artif Intell Med; 2011 Oct; 53(2):83-95. PubMed ID: 21852077
[TBL] [Abstract][Full Text] [Related]
8. A linear-RBF multikernel SVM to classify big text corpora.
Romero R; Iglesias EL; Borrajo L
Biomed Res Int; 2015; 2015():878291. PubMed ID: 25879039
[TBL] [Abstract][Full Text] [Related]
9. Building sparse multiple-kernel SVM classifiers.
Hu M; Chen Y; Kwok JT
IEEE Trans Neural Netw; 2009 May; 20(5):827-39. PubMed ID: 19342346
[TBL] [Abstract][Full Text] [Related]
10. Classification of fruits using computer vision and a multiclass support vector machine.
Zhang Y; Wu L
Sensors (Basel); 2012; 12(9):12489-505. PubMed ID: 23112727
[TBL] [Abstract][Full Text] [Related]
11. SVM-RFE: selection and visualization of the most relevant features through non-linear kernels.
Sanz H; Valim C; Vegas E; Oller JM; Reverter F
BMC Bioinformatics; 2018 Nov; 19(1):432. PubMed ID: 30453885
[TBL] [Abstract][Full Text] [Related]
12. An Efficient PCA-GA-HKSVM-Based Disease Diagnostic Assistant.
Jerop B; Segera DR
Biomed Res Int; 2021; 2021():4784057. PubMed ID: 34722764
[TBL] [Abstract][Full Text] [Related]
13. Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests.
Xin N; Gu XF; Wu H; Hu YZ; Yang ZL
Spectrochim Acta A Mol Biomol Spectrosc; 2012 Apr; 89():18-24. PubMed ID: 22240232
[TBL] [Abstract][Full Text] [Related]
14. Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels.
Utkin LV; Chekh AI; Zhuk YA
Neural Netw; 2016 Aug; 80():53-66. PubMed ID: 27179616
[TBL] [Abstract][Full Text] [Related]
15. Determination of Meta-Parameters for Support Vector Machine Linear Combinations.
Jasial S; Balfer J; Vogt M; Bajorath J
Mol Inform; 2015 Feb; 34(2-3):127-33. PubMed ID: 27490035
[TBL] [Abstract][Full Text] [Related]
16. Interpreting support vector machines applied in laser-induced breakdown spectroscopy.
Képeš E; Vrábel J; Adamovsky O; Střítežská S; Modlitbová P; Pořízka P; Kaiser J
Anal Chim Acta; 2022 Feb; 1192():339352. PubMed ID: 35057964
[TBL] [Abstract][Full Text] [Related]
17. Asymptotic behaviors of support vector machines with Gaussian kernel.
Keerthi SS; Lin CJ
Neural Comput; 2003 Jul; 15(7):1667-89. PubMed ID: 12816571
[TBL] [Abstract][Full Text] [Related]
18. Forecasting performance of support vector machine for the Poyang Lake's water level.
Lan Y
Water Sci Technol; 2014; 70(9):1488-95. PubMed ID: 25401312
[TBL] [Abstract][Full Text] [Related]
19. Support vector machine implementations for classification & clustering.
Winters-Hilt S; Yelundur A; McChesney C; Landry M
BMC Bioinformatics; 2006 Sep; 7 Suppl 2(Suppl 2):S4. PubMed ID: 17118147
[TBL] [Abstract][Full Text] [Related]
20. Identification of fetal sufferance antepartum through a multiparametric analysis and a support vector machine.
Magenes G; Pedrinazzi L; Signorini MG
Conf Proc IEEE Eng Med Biol Soc; 2004; 2006():462-5. PubMed ID: 17271713
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
