Publications
- Zhang, H. H. and Genton, G. M. and Liu, P. (2004) Compactly Supported Radial Basis Function Kernels.
- Zhang, H. H. and Lin, Y. (2004) Component Selection and Smoothing for Nonparametric Regression in Exponential Families. Institute of Statistics Mimeo Series 2554, NCSU.Statistica Sinica, in press.
- Ferris M., Voelker, M, and Zhang, H. H. (2004) Model Building with Likelihood Basis Pursuit (pdf). Institute of Statistics Mimeo Series 2555, NCSU. Journal of Optimization Methods and Software, 19(5), 577-594.
- Lin, Y. and Zhang, H. H. (2003) Component Selection and Smoothing in Smoothing Spline Analysis of Variance Models -- COSSO (ps). Institute of Statistics Mimeo Series 2556, NUCS. Revision invited by journal.
- Zhang, H. H. (2003) Variable Selection for SVM via Basis Pursuit in Smoothing Spline ANOVA models. Institute of Statistics Mimeo Series 2557, NCSU.
- Zhang, H. H., Wahba, G., Lin, Y., Voelker, M., Ferris, M., Klein, R. and Klein, B. (2003) Variable Selection and Model Building via Likelihood Basis Pursuit (ps) , final (pdf). Institute of Statistics Mimeo Series 2558, NCSU. Journal of American Statistical Association, 99, 659-672.
- Lin, Y., Wahba, G., Zhang, H. H., and Lee, Y. (2002) Statistical Properties and Adaptive Tuning of Support Vector Machines. Journal of Machine Learning 48, 115-136.
- Wahba, G., Lin, Y., Lee, Y. and Zhang, H. H. (2002) Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machines. Nonlinear Estimation and Classification, Denison, Hansen, Holmes, Mallick and Yu, eds, Springer, 125-143.
- Zhang, H. H. (2002) Nonparametric Variable Selection and Model Building via Likelihood Basis Pursuit. Ph.D. Thesis, 2002.
- Zhang, H. H., Wahba, G., Lin, Y., Voelker, M., Ferris, M., Klein, R. and Klein, B. (2001) Variable Selection via Basis Pursuit for Non-Gaussian Data. Proceedings of the Joint Statistical Meetings
- Wahba, G., Lin, Y., Lee, Y. and Zhang, H. H. (2001) On the Relation between the GACV and Joachims' \xi\alpha method for tuning Support Vector Machines, with extension to the nonstandard case. UW-Madison TR 1039.
- Wahba, G., Lin, Y. and Zhang, H. H. (2000) Generalized Approximate Cross Validation for Support Vector Machines, or, Another Way to Look at Margin-like Quantities. Advances in Large Margin Classifiers, Smola, Bartlett, Scholkopf and Schurmans, eds., MIT Press, 297-309.
Software