Alireza Tamaddoni-Nezhad
Alireza Tamaddoni-Nezhad
Reader (Associate Professor), University of Surrey
Verified email at - Homepage
Cited by
Cited by
Inductive logic programming
S Muggleton, R Otero, A Tamaddoni-Nezhad
Academic Press, 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
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
Meta-interpretive learning: application to grammatical inference
SH Muggleton, D Lin, N Pahlavi, A Tamaddoni-Nezhad
Machine learning 94, 25-49, 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
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
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
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
Networking our way to better ecosystem service provision
Quintessence Consortium
Trends in Ecology & Evolution 31 (2), 105-115, 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
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
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
Progolem: A system based on relative minimal generalisation
S Muggleton, J Santos, A Tamaddoni-Nezhad
International Conference on Inductive Logic Programming, 131-148, 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
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
Toplog: Ilp using a logic program declarative bias
SH Muggleton, JCA Santos, A Tamaddoni-Nezhad
International Conference on Logic Programming, 687-692, 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
QG/GA: a stochastic search for Progol
S Muggleton, A Tamaddoni-Nezhad
Machine Learning 70, 121-133, 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
Meta-interpretive learning of data transformation programs
A Cropper, A Tamaddoni-Nezhad, SH Muggleton
Inductive Logic Programming: 25th International Conference, ILP 2015, Kyoto …, 2016
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