| The connectionist inductive learning and logic programming system ASA Garcez, G Zaverucha Applied Intelligence 11 (1), 59-77, 1999 | 160 | 1999 |
| Neural-symbolic learning and reasoning: A survey and interpretation TR Besold, AA Garcez, S Bader, H Bowman, P Domingos, P Hitzler, ... arXiv preprint arXiv:1711.03902, 2017 | 87 | 2017 |
| Fast relational learning using bottom clause propositionalization with artificial neural networks MVM França, G Zaverucha, ASA Garcez Machine learning 94 (1), 81-104, 2014 | 84 | 2014 |
| A distribution design methodology for object DBMS F Baião, M Mattoso, G Zaverucha Distributed and Parallel Databases 16 (1), 45-90, 2004 | 57 | 2004 |
| A multi-objective optimization approach accurately resolves protein domain architectures JS Bernardes, FRJ Vieira, G Zaverucha, A Carbone Bioinformatics 32 (3), 345-353, 2016 | 36 | 2016 |
| Object oriented design expertise reuse: An approach based on heuristics, design patterns and anti-patterns AL Correa, CML Werner, G Zaverucha International Conference on Software Reuse, 336-352, 2000 | 35 | 2000 |
| Evaluation and improvements of clustering algorithms for detecting remote homologous protein families JS Bernardes, FRJ Vieira, LMM Costa, G Zaverucha BMC bioinformatics 16 (1), 1-14, 2015 | 29 | 2015 |
| Logical inference and inductive learning in artificial neural networks ASA Garcez, G Zaverucha, LAV de Carvalho Knowledge Representation in Neural Networks, 33-46, 1997 | 28 | 1997 |
| Improvement in protein domain identification is reached by breaking consensus, with the agreement of many profiles and domain co-occurrence J Bernardes, G Zaverucha, C Vaquero, A Carbone PLoS computational biology 12 (7), e1005038, 2016 | 27 | 2016 |
| Improving model construction of profile HMMs for remote homology detection through structural alignment JS Bernardes, AMR Dávila, VS Costa, G Zaverucha BMC bioinformatics 8 (1), 1-12, 2007 | 27 | 2007 |
| Horizontal fragmentation in object dbms: New issues and performance evaluation F Baião, M Mattoso, G Zaverucha Conference Proceedings of the 2000 IEEE International Performance, Computing …, 2000 | 27 | 2000 |
| Probabilistic first-order theory revision from examples A Paes, K Revoredo, G Zaverucha, VS Costa International Conference on Inductive Logic Programming, 295-311, 2005 | 26 | 2005 |
| Artificial neural networks for power systems diagnosis V Navarro, AL da Silva, LAV de Carvalho, G Zaverucha Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN …, 1994 | 24 | 1994 |
| Towards an inductive design of distributed object oriented databases F Baido, M Mattoso, G Zaverucha Proceedings. 3rd IFCIS International Conference on Cooperative Information …, 1998 | 23 | 1998 |
| Learning logic programs with neural networks R Basilio, G Zaverucha, VC Barbosa International Conference on Inductive Logic Programming, 15-26, 2001 | 22 | 2001 |
| Using the bottom clause and mode declarations in FOL theory revision from examples AL Duboc, A Paes, G Zaverucha Machine learning 76 (1), 73-107, 2009 | 21 | 2009 |
| Chess revision: Acquiring the rules of chess variants through FOL theory revision from examples S Muggleton, A Paes, VS Costa, G Zaverucha International Conference on Inductive Logic Programming, 123-130, 2009 | 19 | 2009 |
| Applying theory revision to the design of distributed databases F Baião, M Mattoso, J Shavlik, G Zaverucha International Conference on Inductive Logic Programming, 57-74, 2003 | 18 | 2003 |
| Normal programs and multiple predicate learning L Fogel, G Zaverucha International Conference on Inductive Logic Programming, 175-184, 1998 | 17 | 1998 |
| Htilde: scaling up relational decision trees for very large databases C Lopes, G Zaverucha Proceedings of the 2009 ACM symposium on Applied Computing, 1475-1479, 2009 | 16 | 2009 |
