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Items: 1 to 20 of 62

1.

A study on L2-loss (squared hinge-loss) multiclass SVM.

Lee CP, Lin CJ.

Neural Comput. 2013 May;25(5):1302-23. doi: 10.1162/NECO_a_00434. Epub 2013 Mar 7.

PMID:
23470126
2.

[Fetal hemoglobin and the gamma G/gamma A chain ratio in children with acute lymphoblastic leukemia L1 and L2].

Villalobos-Arámbula AR, Aguilar-Luna JC, Esparza A, Perea FJ, de Loza R, Hernández-Córdova A, Ibarra B.

Sangre (Barc). 1993 Feb;38(1):31-5. Spanish.

PMID:
7682338
3.

Arbitrary norm support vector machines.

Huang K, Zheng D, King I, Lyu MR.

Neural Comput. 2009 Feb;21(2):560-82. doi: 10.1162/neco.2008.12-07-667.

PMID:
19431269
4.

Radius margin bounds for support vector machines with the RBF kernel.

Chung KM, Kao WC, Sun CL, Wang LL, Lin CJ.

Neural Comput. 2003 Nov;15(11):2643-81.

PMID:
14577857
5.

A formal analysis of stopping criteria of decomposition methods for support vector machines.

Lin CJ.

IEEE Trans Neural Netw. 2002;13(5):1045-52. doi: 10.1109/TNN.2002.1031937.

PMID:
18244502
6.

Geometrical properties of nu support vector machines with different norms.

Ikeda K, Murata N.

Neural Comput. 2005 Nov;17(11):2508-29.

PMID:
16156937
7.

Multivariate calibration with least-squares support vector machines.

Thissen U, Ustün B, Melssen WJ, Buydens LM.

Anal Chem. 2004 Jun 1;76(11):3099-105.

PMID:
15167788
8.

Diagnosis of airway obstruction or restrictive spirometric patterns by multiclass support vector machines.

Sahin D, Ubeyli ED, Ilbay G, Sahin M, Yasar AB.

J Med Syst. 2010 Oct;34(5):967-73. doi: 10.1007/s10916-009-9312-7. Epub 2009 May 12.

PMID:
20703611
9.

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. doi: 10.1109/TSMCB.2008.927272.

PMID:
19022717
10.

Quantum optimization for training support vector machines.

Anguita D, Ridella S, Rivieccio F, Zunino R.

Neural Netw. 2003 Jun-Jul;16(5-6):763-70.

PMID:
12850032
11.

Fuzzy least squares support vector machines for multiclass problems.

Tsujinishi D, Abe S.

Neural Netw. 2003 Jun-Jul;16(5-6):785-92.

PMID:
12850035
12.

MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data.

Zhou X, Tuck DP.

Bioinformatics. 2007 May 1;23(9):1106-14. Erratum in: Bioinformatics. 2007 Aug;23(15):2029.

PMID:
17494773
13.

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. doi: 10.1109/TSMCB.2011.2168604. Epub 2011 Oct 6.

PMID:
21984515
14.

The sound level of the singer's formant in professional singing.

Bloothooft G, Plomp R.

J Acoust Soc Am. 1986 Jun;79(6):2028-33.

PMID:
3722610
15.

Asymptotic behaviors of support vector machines with Gaussian kernel.

Keerthi SS, Lin CJ.

Neural Comput. 2003 Jul;15(7):1667-89.

PMID:
12816571
16.

A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection.

Luts J, Heerschap A, Suykens JA, Van Huffel S.

Artif Intell Med. 2007 Jun;40(2):87-102. Epub 2007 Apr 26.

PMID:
17466495
17.

L2 kernel classification.

Kim J, Scott CD.

IEEE Trans Pattern Anal Mach Intell. 2010 Oct;32(10):1822-31. doi: 10.1109/TPAMI.2009.188.

PMID:
20724759
18.

Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines.

Derya Ubeyli E.

Comput Biol Med. 2008 Jan;38(1):14-22. Epub 2007 Jul 24.

PMID:
17651716
19.

Support vector machines (SVMs) for monitoring network design.

Asefa T, Kemblowski M, Urroz G, McKee M.

Ground Water. 2005 May-Jun;43(3):413-22.

PMID:
15882333
20.

The singer's voice range profile: female professional opera soloists.

Lamarche A, Ternström S, Pabon P.

J Voice. 2010 Jul;24(4):410-26. doi: 10.1016/j.jvoice.2008.12.008. Epub 2009 Oct 17.

PMID:
19837561

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