| Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data Y Lee, Y Lin, G Wahba Journal of the American Statistical Association 99 (465), 67-81, 2004 | 1120 | 2004 |
| Support vector machines for classification in nonstandard situations Y Lin, Y Lee, G Wahba Machine learning 46, 191-202, 2002 | 567 | 2002 |
| Classification of multiple cancer types by multicategory support vector machines using gene expression data Y Lee, CK Lee Bioinformatics 19 (9), 1132-1139, 2003 | 482 | 2003 |
| Dimensionality reduction for binary data through the projection of natural parameters AJ Landgraf, Y Lee Journal of Multivariate Analysis 180, 104668, 2020 | 159 | 2020 |
| A comparison of normalization techniques for microRNA microarray data Y Rao, Y Lee, D Jarjoura, AS Ruppert, C Liu, JC Hsu, JP Hagan Statistical applications in genetics and molecular biology 7 (1), 2008 | 150 | 2008 |
| Cloud classification of satellite radiance data by multicategory support vector machines Y Lee, G Wahba, SA Ackerman Journal of Atmospheric and Oceanic Technology 21 (2), 159-169, 2004 | 112 | 2004 |
| A Bahadur representation of the linear support vector machine JY Koo, Y Lee, Y Kim, C Park Journal of Machine Learning Research 9 (Jul), 1343-1368, 2008 | 77 | 2008 |
| Regularization of case-specific parameters for robustness and efficiency Y Lee, SN MacEachern, Y Jung | 64* | 2012 |
| Statistical properties and adaptive tuning of support vector machines Y Lin, G Wahba, H Zhang, Y Lee Machine Learning 48, 115-136, 2002 | 61 | 2002 |
| Structured multicategory support vector machines with analysis of variance decomposition Y Lee, Y Kim, S Lee, JY Koo Biometrika 93 (3), 555-571, 2006 | 59 | 2006 |
| Generalized principal component analysis: Projection of saturated model parameters AJ Landgraf, Y Lee Technometrics 62 (4), 459-472, 2020 | 46 | 2020 |
| On theoretically optimal ranking functions in bipartite ranking K Uematsu, Y Lee Journal of the American Statistical Association 112 (519), 1311-1322, 2017 | 39* | 2017 |
| Support vector machines for classification: a statistical portrait Y Lee Statistical Methods in Molecular Biology, 347-368, 2010 | 36 | 2010 |
| Bayesian restricted likelihood methods: Conditioning on insufficient statistics in Bayesian regression (with discussion) JR Lewis, SN MacEachern, Y Lee Bayesian Analysis 16 (4), 1393-1462, 2021 | 35 | 2021 |
| Optimal properties and adaptive tuning of standard and nonstandard support vector machines G Wahba, Y Lin, Y Lee, H Zhang Nonlinear estimation and classification, 129-147, 2003 | 31 | 2003 |
| Characterizing the solution path of multicategory support vector machines Y Lee, Z Cui Statistica Sinica, 391-409, 2006 | 30 | 2006 |
| Another look at linear programming for feature selection via methods of regularization Y Yao, Y Lee Statistics and Computing 24 (5), 885-905, 2014 | 28 | 2014 |
| Eigen‐analysis of nonlinear PCA with polynomial kernels Z Liang, Y Lee Statistical Analysis and Data Mining: The ASA Data Science Journal 6 (6 …, 2013 | 27 | 2013 |
| Statistical optimality in multipartite ranking and ordinal regression K Uematsu, Y Lee IEEE transactions on pattern analysis and machine intelligence 37 (5), 1080-1094, 2014 | 23 | 2014 |
| Efficient quantile regression for heteroscedastic models Y Jung, Y Lee, SN MacEachern Journal of Statistical Computation and Simulation 85 (13), 2548-2568, 2015 | 18 | 2015 |
Grace WahbaProfessor of Statistics, University of Wisconsin-MadisonVerified email at stat.wisc.edu
