| An analysis of active learning strategies for sequence labeling tasks B Settles, M Craven proceedings of the 2008 conference on empirical methods in natural language …, 2008 | 1341 | 2008 |
| Extracting tree-structured representations of trained networks M Craven, J Shavlik Advances in neural information processing systems 8, 1995 | 1133 | 1995 |
| Learning to extract symbolic knowledge from the World Wide Web M Craven, D DiPasquo, D Freitag, A McCallum, T Mitchell, K Nigam, ... AAAI/IAAI 3 (3.6), 2, 1998 | 1086 | 1998 |
| Constructing biological knowledge bases by extracting information from text sources. M Craven, J Kumlien ISMB 1999, 77-86, 1999 | 831 | 1999 |
| Multiple-instance active learning B Settles, M Craven, S Ray Advances in neural information processing systems 20, 2007 | 812 | 2007 |
| Learning to construct knowledge bases from the World Wide Web M Craven, D DiPasquo, D Freitag, A McCallum, T Mitchell, K Nigam, ... Artificial intelligence 118 (1-2), 69-113, 2000 | 709 | 2000 |
| Incorporating domain knowledge into topic modeling via Dirichlet forest priors D Andrzejewski, X Zhu, M Craven Proceedings of the 26th annual international conference on machine learning …, 2009 | 556 | 2009 |
| Using Sampling and Queries to Extract Rules from MW Craven, JW Shavlik Machine Learning Proceedings 1994: Proceedings of the Eighth International …, 1994 | 482* | 1994 |
| Using neural networks for data mining MW Craven, JW Shavlik Future generation computer systems 13 (2-3), 211-229, 1997 | 443 | 1997 |
| Extracting comprehensible models from trained neural networks MW Craven The University of Wisconsin-Madison, 1996 | 396 | 1996 |
| Active learning with real annotation costs B Settles, M Craven, L Friedland Proceedings of the NIPS workshop on cost-sensitive learning 1, 2008 | 338 | 2008 |
| Supervised versus multiple instance learning: An empirical comparison S Ray, M Craven Proceedings of the 22nd international conference on Machine learning, 697-704, 2005 | 318 | 2005 |
| Identification of toxicologically predictive gene sets using cDNA microarrays RS Thomas, DR Rank, SG Penn, GM Zastrow, KR Hayes, K Pande, ... Molecular Pharmacology 60 (6), 1189-1194, 2001 | 297 | 2001 |
| Hierarchical hidden markov models for information extraction M Skounakis, M Craven, S Ray IJCAI 2003, 427-433, 2003 | 226 | 2003 |
| Representing sentence structure in hidden Markov models for information extraction S Ray, M Craven International Joint Conference on Artificial Intelligence 17 (1), 1273-1279, 2001 | 194 | 2001 |
| Relational learning with statistical predicate invention: Better models for hypertext M Craven, S Slattery Machine Learning 43, 97-119, 2001 | 181 | 2001 |
| Learning symbolic rules using artificial neural networks MW Craven, JW Shavlik Proceedings of the Tenth International Conference on Machine Learning, 73-80, 1993 | 171* | 1993 |
| A framework for incorporating general domain knowledge into latent dirichlet allocation using first-order logic D Andrzejewski, X Zhu, M Craven, B Recht Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2011 | 161 | 2011 |
| A Bayesian network approach to operon prediction J Bockhorst, M Craven, D Page, J Shavlik, J Glasner Bioinformatics 19 (10), 1227-1235, 2003 | 150 | 2003 |
| Combining statistical and relational methods for learning in hypertext domains S Slattery, M Craven International Conference on Inductive Logic Programming, 38-52, 1998 | 131 | 1998 |
