References
- Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., and Levine, A. (1999), “Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays,” Cell Biology, 96, 6745–6750.
- Chambers, J., and Hastie, T. (1992), Statistical Models in S, Pacific Grove, CA: Wadsworth and Brooks/Cole.
- Hastie, T., Rosset, R., Tibshirani, R., and Zhou, J. (2004), “The Entire Regularization Path for the Support Vector Machine,” Journal of Machine Learning Reasearch, 5, 1931–1415.
- Jaakkola, T., Diekhans, M., and Haussler, D. (1999), “Using the Fisher Kernel Method to Detect Remote Protein Homologies,” in Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, Heidelberg, Germany: AAAI, pp. 149–158.
- Kimeldorf, G., and Wahba, G. (1971), “Some Results on Tchebycheffian Spline Functions,” Journal of Mathematical Analysis and Applications, 33, 82–95.
- Lin, Y. (2002), “Support Vector Machines and the Bayes Rule in Classification,” Data Mining and Knowledge Discovery, 6, 259–275.
- Lin, Y., Lee, Y., and Wahba, G. (2004), “Support Vector Machines for Classification in Nonstandard Situations,” Machine Learning, 46, 191–202.
- Liu, Y. (2007), “Fisher Consistency of Multicategory Support Vector Machines,” in Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, Heidelberg, Germany: AAAI, pp. 289–296.
- Rosset, S., and Zhu, J. (2007), “Piecewise Linear Regularized Solution Paths,” The Annals of Statistics, 35, 1012–1030.
- Vapnik, V. (1996), The Nature of Statistical Learning Theory, New York: Springer-Verlag.
- Wahba, G. (1990), Spline Models for Observational Data, SIAM, CBMS-NSF Regional Conference Series in Applied Mathematics, v. 59.
- ——— (1999), “Support Vector Machines, Reproducing Kernel Hilbert Spaces, and Randomized Gacv,” in Advances in Kernel Methods: Support Vector Learning, eds. B. Schölkopf, C. J. C. Burges, and A. J. Smola, Cambridge, MA: MIT Press, pp. 125–143.
- Wang, J., Shen, X., and Liu, Y. (2008), “Probability Estimation for Large-Margin Classifier,” Biometrika, 95, 149–167.
- Wang, L., and Shen, X. (2007), “On l1-norm Multi-Class Support Vector Machines: Methodology and Theory,” Journal of the American Statistical Association, 102, 595–602.
- West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Olson, J. A.Jr, Marks, J., and Nevins, J. (2001), “Predicting the Clinical Status of Human Breast Cancer by Using Gene Expression Profiles,” Proceedings of the National Academy of Sciences, 98, 11462–11467.
- Zhang, H.H., Ahn, J., Lin, X., and Park, C. (2006), “Gene Selection Using Support Vector Machines With Nonconvex Penalty,” Bioinformatics, 22, 88–95.
- Zhu, J., Rosset, S., Hastie, T., and Tibshirani, R. (2003), “1-norm Support Vector Machines,” Advances in Neural Information Processing Systems, 16, 49–56.