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publications by dr. ir. P.L. Geenen

Petra  Geenen

dr. ir. P.L. Geenen

some publications

Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2011). Constructing naive Bayesian classifiers for veterinary medicine: A case study in the clinical diagnosis of classical swine fever in. Research in Veterinary Science, 91, 64-70.

Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2009). Constructing naive Bayesian classifiers for veterinary medicine: a case study in the clinical diagnosis of classical swine fever. onbekend: UU BETA ICS Departement Informatica.

Tabachneck-Schijf, H.J.M., Gaag, L.C. van der, Geenen, P.L., Schrage, M., Loeffen, W.L.A. & Elbers, A.R.W. (2008). Designing a personal digital assistant for early on-site detection of classical swine fever in a pig unit. In P. Evans (Ed.), Proceedings of the 20th International Pig Veterinary Science Congress. Durban, South Africa: Congress.

Gaag, L.C. van der, Tabachneck-Schijf, H.J.M. & Geenen, P.L. (2008). Verifying Monotonicity of Bayesian Networks with Domain Experts. UU-CS 2008-014. onbekend: UU WINFI Informatica en Informatiekunde.

Tabachneck-Schijf, H.J.M. & Geenen, P.L. (2006). Preventing knowledge transfer errors: probabilistic decision support systems through the users’ eyes. In L.C. van der Gaag & R. Almond (Eds.), Proceedings of the 4th Bayesian Modelling Applications Workshop: Bayesian Models Meet Cognition (pp. 74-82).

Geenen, P.L., Elbers, A.R.W., Gaag, L.C. van der & Loeffen, W.L.A. (2006). Development of a probabilistic network for clinical detection of classical swine fever. In Proceedings of the 11th Symposium of the International Society for Veterinary Epidemiology and Economics (pp. 667-669). Cairns, Australia.

Gaag, L.C. van der, Renooij, S. & Geenen, P.L. (2006). Lattices for studying monotonicity of Bayesian networks. In M. Studeny & J. Vomlel (Eds.), Proceedings of the Third European Workshop on Probabilistic Graphical Models (pp. 99-106). Prague, Czech Republic.

Gaag, L.C. van der, Geenen, P.L. & Tabachneck-Schijf, H.J.M. (2006). Verifying monotonicity in Bayesian networks with domain experts. In L.C. van der Gaag & R. Almond (Eds.), Proceedings of the 4th Bayesian Modelling Applications Workshop: Bayesian Models Meet Cognition (pp. 9-15).

Helsper, E.M., Gaag, L.C. van der, Feelders, A.J., Loeffen, W.L.A., Geenen, P.L. & Elbers, A.R.W. (2005). Bringing order into Bayesian-network construction. In Proceedings of the Third International Conference on Knowledge Capture (pp. 121-128). New York: ACM Press.

Geenen, P.L. & Gaag, L.C. van der (2005). Developing a Bayesian network for clinical diagnosis in veterinary medicine: from the individual to the herd. In Proceedings of the Third Bayesian Modelling Applications Workshop, held in conjunction with the Twenty-first Conference on Uncertainty in Artificial Intelligence. Edinburgh.

Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2005). Naive Bayesian classifiers for the clinical diagnosis of Classical Swine Fever. In D.J. Mellor, A.M. Russell & J.L.N. Wood (Eds.), Proceedings of the Meeting of the Society for Veterinary Epidemiology and Preventive Medicine (pp. 169-176). Nairn,Schotland.

Geenen, P.L., Döpfer, D.D.V., Meulen, J. van der & Jong, M.C.M. de (2005). Transmission of F4+ E.coli in groups of early weaned piglets. Epidemiology and Infection, 133(3), 459-468.

Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2004). Building naive Bayesian classifiers from literature: a case study in classical swine fever. In R. Verbrugge, N. Taagten & L. Schomaker (Eds.), Proceedings of the Sixteenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 227-234). Groningen: University of Groningen.

Geenen, P.L., Meulen, J. van der, Bouma, A. & Jong, M.C.M. de (2004). Estimating transmission parameters of F4 + E. coli for F4-receptor-positive and-negative piglets: one-to-one transmission experiment. Epidemiology and Infection, 132, 1039-1048.

Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2004). On the robustness of feature selection with absent and non-observed features. In J.M. Barreiro, F. Martin-Sanchez, V. Maojo & F. Sanz (Eds.), Proceedings of the Fifth International Symposium on Biological and Medical Data Analysis (pp. 148-159). Heidelberg: Springer-Verlag.


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