1 |
G. Cohen, M. Hilario, H. Sax, S. Hugonnet, and A. Geissbuhler, "Learning from imbalanced data in surveillance of nosocomial infection," Artificial Intelligence in Medicine, Vol.37, pp. 7-18, 2006.
DOI
|
2 |
T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler, "Support vector machine classification and validation of cancer tissue samples using microarray expression data," Bioirformatics, Vol.16, pp. 906-914, 2000.
DOI
ScienceOn
|
3 |
C. Cortes and V. Vapnik, "Support vector networks," Machine Learning, Vol.20, pp. 273-297, 1995.
|
4 |
V. Vapnik, The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1995.
|
5 |
J. H. OH, J. Gao, A. nandi. P. Gurnani, L. Knowles, J. Schorge, and K. P. Rosenblatt. "Multicategory classification using extended SVM-RFE and markov blanket on SELDI-TOF mass spectrometry data," the Institute of Electrical and Electronics Engineers Symposium. Computational Intelligence in Bioinformatics and Computational Biology, 2005.
|
6 |
M. E. Mavroforakix, H. V. Georgiou, N. Dimitropuoulox, D. Cavoura, and S. Theodoridis, "Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers," Artificial Intelligence in Medicine, Vol.37, pp. 145-162, 2006.
DOI
|
7 |
L. Ramirez, N. G. Durdle, V. J. Raso, and D. L. Hill, "A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topology," the Institute of Electrical and Electronics Engineers Transactions on Information Technology in Biomedicine, Vol.10, No.1, pp. 84-91, 2006.
|
8 |
K. Takeuchi and N. Collier, "Bio-medical entity extraction using support vector machines," Artificial Intelligence in Medicine, Vol.33, pp. 125-137, 2005.
DOI
|
9 |
T.Arodz, M. Kurdziel, E. O. D. Sevre, and D. A. Yuen, "Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms," Computer Methods and Programs in Biomedicine, Vol.79, pp. 135-149, 2005.
DOI
ScienceOn
|
10 |
B. Cho, H. Yu, J. Lee, Y. Chee, and I. Kim, "Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels," the Institute of Electrical and Electronics Engineers Transactions on Information Technology in Biomedicine, 2005.
|
11 |
A. Jakulin, M. Mozina, J. Demsar, I. Bratko, and B. Zupan, "Nomograms for visualizing support vector machines," Knowledge Discouery and Data Mining, 2005.
|
12 |
C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining Knowledge Discovery, Vol.2, pp. 121-167, 1998.
DOI
ScienceOn
|
13 |
C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines [Online]," Available: http://www.csie.ntu.edu.tw/-cjlin/libsvm, 2001.
|
14 |
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Machine Learning, Vol.46, pp. 389-422, 2002.
DOI
|
15 |
I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," the Journal of Machine Learning Research, Vol.3, pp. 1157-1182, 2003.
|