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

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[1] [2] [3] [4] [5] [6]

  1. 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.
  2. 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/
  3. 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
  4. Dzeroski, S. 2002. Environmental Applications of Data Mining. Retrieved from http://rses.anu.edu.au/cadi/Whiteconference/papers/DzeroskiAbstract.pdf
  5. Dzeroski, S., Todorovski, L. 2003. Learning population dynamics models from data and domain knowledge. Ecological Modelling, 170, pp. 129--140
  6. Phillips, S. J., Anderson, R. P., Schapire, R. E. 2006. “Maximum entropy modeling of species geographic distributions,” Ecological Modelling, no. 190, pp. 231–259.