| Support vector machine soft margin classifiers: error analysis DR Chen, Q Wu, Y Ying, DX Zhou Journal of Machine Learning Research 5 (Sep), 1143-1175, 2004 | 314 | 2004 |
| Learning rates of least-square regularized regression Q Wu, Y Ying, DX Zhou Foundations of Computational Mathematics 6 (2), 171-192, 2006 | 279 | 2006 |
| SVM soft margin classifiers: linear programming versus quadratic programming Q Wu, DX Zhou Neural computation 17 (5), 1160-1187, 2005 | 223 | 2005 |
| Multi-kernel regularized classifiers Q Wu, Y Ying, DX Zhou Journal of Complexity 23 (1), 108-134, 2007 | 213 | 2007 |
| Analysis of Support Vector Machine Classification. Q Wu, DX Zhou Journal of Computational Analysis & Applications 8 (2), 2006 | 146 | 2006 |
| Least square regression with indefinite kernels and coefficient regularization H Sun, Q Wu Applied and Computational Harmonic Analysis 30 (1), 96-109, 2011 | 120 | 2011 |
| Learning with sample dependent hypothesis spaces Q Wu, DX Zhou Computers & Mathematics with Applications 56 (11), 2896-2907, 2008 | 111 | 2008 |
| Estimation of gradients and coordinate covariation in classification S Mukherjee, Q Wu Journal of Machine Learning Research 7 (Nov), 2481-2514, 2006 | 94 | 2006 |
| Learning theory approach to minimum error entropy criterion T Hu, J Fan, Q Wu, DX Zhou Journal of Machine Learning Research 14 (Feb), 377-397, 2013 | 93 | 2013 |
| Boundedness of commutators on Hardy type spaces S Lu, Q Wu, D Yang Science in China Series A: Mathematics 45 (8), 984-997, 2002 | 92 | 2002 |
| Regularization schemes for minimum error entropy principle T Hu, J Fan, Q Wu, DX Zhou Analysis and Applications 13 (04), 437-455, 2015 | 82 | 2015 |
| Characterizing the function space for Bayesian kernel models NS Pillai, Q Wu, F Liang, S Mukherjee, RL Wolpert Journal of Machine Learning Research 8 (Aug), 1769-1797, 2007 | 82 | 2007 |
| Regularized least square regression with dependent samples H Sun, Q Wu Advances in Computational Mathematics 32 (2), 175-189, 2010 | 77 | 2010 |
| Learning gradients on manifolds S Mukherjee, Q Wu, DX Zhou Bernoulli 16 (1), 181-207, 2010 | 68 | 2010 |
| Localized sliced inverse regression Q Wu, S Mukherjee, F Liang Advances in neural information processing systems, 1785-1792, 2009 | 64 | 2009 |
| Localized sliced inverse regression Q Wu, S Mukherjee, F Liang Advances in neural information processing systems, 1785-1792, 2009 | 64 | 2009 |
| Application of integral operator for regularized least-square regression H Sun, Q Wu Mathematical and Computer Modelling 49 (1-2), 276-285, 2009 | 60 | 2009 |
| Consistency analysis of an empirical minimum error entropy algorithm J Fan, T Hu, Q Wu, DX Zhou Applied and Computational Harmonic Analysis 41 (1), 164-189, 2016 | 56 | 2016 |
| Learning theory of distributed regression with bias corrected regularization kernel network ZC Guo, L Shi, Q Wu The Journal of Machine Learning Research 18 (1), 4237-4261, 2017 | 51 | 2017 |
| Learning gradients: predictive models that infer geometry and statistical dependence Q Wu, J Guinney, M Maggioni, S Mukherjee Journal of Machine Learning Research 11 (Aug), 2175-2198, 2010 | 50 | 2010 |
Yiming YingSchool of Mathematics and Statistics, University of Sydney在 sydney.edu.au 的电子邮件经过验证
