Artificial Intelligence for Computational Sustainability: A Lab Companion/Detailed bibliography on computational sustainability

Todorovski, L., Džeroski, S., 1997. Declarative bias in equation discovery. In: Proceedings of the Fourteenth International Conference on Machine Learning. Morgan Kaufmann, Los Altos, CA, pp. 376–384. Aspinall, R., 1992. An inductive modeling procedure based on Bayes’ theorem for analysis of pattern in spatial data. Int. J. Geogr. Inform. Syst. 6 (2), 105–121. Retrieved from http://www.mendeley.com/research/inductive-modelling-procedure-based-bayes-theorem-analysis-pattern-spatial-data-1/ Serra, M. Sànchez-Marrè, J. Lafuente, U. Cortés, M. Poch ISCWAP: a knowledge-based system for supervising activated sludge processes Comput. Chem. Eng., 21 (2), pp. 211–221. Retrieved from http://ac.els-cdn.com/0098135495002588/1-s2.0-0098135495002588-main.pdf?_tid=3c1656c5c7d8e18b440147c4ca186dc1&acdnat=1340798089_e2819310b4d62981aaeaa7bdb3cc072d Dzeroski, S. 2002. Environmental Applications of Data Mining. Retrieved from http://rses.anu.edu.au/cadi/Whiteconference/papers/DzeroskiAbstract.pdf Dzeroski, S., Todorovski, L. 2003. Learning population dynamics models from data and domain knowledge. Ecological Modelling, 170, pp. 129--140 Phillips, S. J., Anderson, R. P., Schapire, R. E. 2006. “Maximum entropy modeling of species geographic distributions,” Ecological Modelling, no. 190, pp. 231–259.