| Inductive logic programming S Muggleton, R Otero, A Tamaddoni-Nezhad Academic Press, 2006 | 1011* | 2006 |
| Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited SH Muggleton, D Lin, A Tamaddoni-Nezhad Machine Learning 100 (1), 49-73, 2015 | 271 | 2015 |
| Next-generation global biomonitoring: large-scale, automated reconstruction of ecological networks DA Bohan, C Vacher, A Tamaddoni-Nezhad, A Raybould, AJ Dumbrell, ... Trends in ecology & evolution 32 (7), 477-487, 2017 | 193 | 2017 |
| Meta-interpretive learning: application to grammatical inference SH Muggleton, D Lin, N Pahlavi, A Tamaddoni-Nezhad Machine learning 94, 25-49, 2014 | 162 | 2014 |
| Ultra-strong machine learning: comprehensibility of programs learned with ILP SH Muggleton, U Schmid, C Zeller, A Tamaddoni-Nezhad, T Besold Machine Learning 107, 1119-1140, 2018 | 128 | 2018 |
| Networking agroecology: integrating the diversity of agroecosystem interactions DA Bohan, A Raybould, C Mulder, G Woodward, A Tamaddoni-Nezhad, ... Advances in ecological research 49, 1-67, 2013 | 116 | 2013 |
| Application of abductive ILP to learning metabolic network inhibition from temporal data A Tamaddoni-Nezhad, R Chaleil, A Kakas, S Muggleton Machine Learning 64, 209-230, 2006 | 116 | 2006 |
| Learning ecological networks from next-generation sequencing data C Vacher, A Tamaddoni-Nezhad, S Kamenova, N Peyrard, Y Moalic, ... Advances in ecological research 54, 1-39, 2016 | 114 | 2016 |
| Networking our way to better ecosystem service provision Quintessence Consortium Trends in Ecology & Evolution 31 (2), 105-115, 2016 | 95 | 2016 |
| Automated discovery of food webs from ecological data using logic-based machine learning DA Bohan, G Caron-Lormier, S Muggleton, A Raybould, ... PLoS One 6 (12), e29028, 2011 | 88 | 2011 |
| Key questions for next-generation biomonitoring A Makiola, ZG Compson, DJ Baird, MA Barnes, SP Boerlijst, A Bouchez, ... Frontiers in Environmental Science 7, 197, 2020 | 79 | 2020 |
| The visualisation of ecological networks, and their use as a tool for engagement, advocacy and management MJO Pocock, DM Evans, C Fontaine, M Harvey, R Julliard, Ó McLaughlin, ... Advances in ecological research 54, 41-85, 2016 | 74 | 2016 |
| Progolem: A system based on relative minimal generalisation S Muggleton, J Santos, A Tamaddoni-Nezhad International Conference on Inductive Logic Programming, 131-148, 2009 | 68 | 2009 |
| Construction and validation of food webs using logic-based machine learning and text mining A Tamaddoni-Nezhad, GA Milani, A Raybould, S Muggleton, DA Bohan Advances in Ecological Research 49, 225-289, 2013 | 62 | 2013 |
| How does predicate invention affect human comprehensibility? U Schmid, C Zeller, T Besold, A Tamaddoni-Nezhad, S Muggleton Inductive Logic Programming: 26th International Conference, ILP 2016, London …, 2017 | 52 | 2017 |
| Toplog: Ilp using a logic program declarative bias SH Muggleton, JCA Santos, A Tamaddoni-Nezhad International Conference on Logic Programming, 687-692, 2008 | 51 | 2008 |
| The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause A Tamaddoni-Nezhad, S Muggleton Machine learning 76, 37-72, 2009 | 44 | 2009 |
| QG/GA: a stochastic search for Progol S Muggleton, A Tamaddoni-Nezhad Machine Learning 70, 121-133, 2008 | 40 | 2008 |
| Meta-interpretive learning from noisy images S Muggleton, WZ Dai, C Sammut, A Tamaddoni-Nezhad, J Wen, ZH Zhou Machine Learning 107, 1097-1118, 2018 | 38 | 2018 |
| Meta-interpretive learning of data transformation programs A Cropper, A Tamaddoni-Nezhad, SH Muggleton Inductive Logic Programming: 25th International Conference, ILP 2015, Kyoto …, 2016 | 37 | 2016 |
Stephen MuggletonImperial College London在 imperial.ac.uk 的电子邮件经过验证
