| Proximal support vector machine classifiers G Fung, OL Mangasarian Proceedings of the seventh ACM SIGKDD international conference on Knowledge …, 2001 | 1472 | 2001 |
| Nyströmformer: A nyström-based algorithm for approximating self-attention Y Xiong, Z Zeng, R Chakraborty, M Tan, G Fung, Y Li, V Singh Proceedings of the AAAI conference on artificial intelligence 35 (16), 14138 …, 2021 | 720 | 2021 |
| Fast optimization methods for l1 regularization: A comparative study and two new approaches M Schmidt, G Fung, R Rosales European Conference on Machine Learning, 286-297, 2007 | 509 | 2007 |
| Active learning from crowds Y Yan, GM Fung, R Rosales, JG Dy Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 436 | 2011 |
| A feature selection Newton method for support vector machine classification GM Fung, OL Mangasarian Computational optimization and applications 28 (2), 185-202, 2004 | 396 | 2004 |
| On the dangers of cross-validation. An experimental evaluation RB Rao, G Fung, R Rosales Proceedings of the 2008 SIAM international conference on data mining, 588-596, 2008 | 343 | 2008 |
| Semi-superyised support vector machines for unlabeled data classification G Fung, OL Mangasarian Optimization methods and software 15 (1), 29-44, 2001 | 299 | 2001 |
| Modeling annotator expertise: Learning when everybody knows a bit of something Y Yan, R Rosales, G Fung, M Schmidt, G Hermosillo, L Bogoni, L Moy, ... Proceedings of the thirteenth international conference on artificial …, 2010 | 272 | 2010 |
| Knowledge-based support vector machine classifiers G Fung, O Mangasarian, J Shavlik Advances in neural information processing systems 15, 2002 | 269 | 2002 |
| Learning from multiple annotators with varying expertise Y Yan, R Rosales, G Fung, R Subramanian, J Dy Machine learning 95 (3), 291-327, 2014 | 265 | 2014 |
| Systems and methods for automated diagnosis and decision support for breast imaging S Krishnan, RB Rao, M Dundar, G Fung US Patent 7,640,051, 2009 | 250 | 2009 |
| Rule extraction from linear support vector machines G Fung, S Sandilya, RB Rao Proceedings of the eleventh ACM SIGKDD international conference on Knowledge …, 2005 | 234 | 2005 |
| Incremental support vector machine classification G Fung, OL Mangasarian Proceedings of the 2002 SIAM International Conference on Data Mining, 247-260, 2002 | 222 | 2002 |
| SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information G Fung, J Stoeckel Knowledge and Information Systems 11 (2), 243-258, 2007 | 212 | 2007 |
| A comprehensive overview of basic clustering algorithms G Fung | 196 | 2001 |
| From transformation-based dimensionality reduction to feature selection M Masaeli, JG Dy, GM Fung Proceedings of the 27th international conference on machine learning (ICML …, 2010 | 189 | 2010 |
| Predicting readmission risk with institution-specific prediction models S Yu, F Farooq, A Van Esbroeck, G Fung, V Anand, B Krishnapuram Artificial intelligence in medicine 65 (2), 89-96, 2015 | 172 | 2015 |
| Structure learning in random fields for heart motion abnormality detection M Schmidt, K Murphy, G Fung, R Rosales 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008 | 161 | 2008 |
| Finite Newton method for Lagrangian support vector machine classification G Fung, OL Mangasarian Neurocomputing 55 (1-2), 39-55, 2003 | 155 | 2003 |
| Multiple instance learning for computer aided diagnosis M Dundar, B Krishnapuram, R Rao, G Fung Advances in neural information processing systems 19, 2006 | 148 | 2006 |
Bharat RaoCarenosticsVerified email at carenostics.com
