TY - CHAP A1 - Schön, Torsten A1 - Stetter, Martin A1 - Lang, Elmar W. ED - Wani, M. Arif ED - Khoshgoftaar, Taghi ED - Zhu, Xingquan ED - Seliya, Naeem T1 - Structure Learning for Bayesian Networks Using the Physarum Solver T2 - Proceedings, 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, Volume 2 UR - https://doi.org/10.1109/ICMLA.2012.89 Y1 - 2012 UR - https://doi.org/10.1109/ICMLA.2012.89 SN - 978-1-4673-4651-1 SP - 488 EP - 493 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Schön, Torsten A1 - Stetter, Martin A1 - Belova, O. A1 - Koch, A. A1 - Tomé, Ana Maria A1 - Lang, Elmar W. ED - Adamatzky, Andrew T1 - Physarum Learner: A Slime Mold Inspired Structural Learning Approach T2 - Advances in Physarum Machines: Sensing and Computing with Slime Mould UR - https://doi.org/10.1007/978-3-319-26662-6_25 Y1 - 2016 UR - https://doi.org/10.1007/978-3-319-26662-6_25 SN - 978-3-319-26662-6 SN - 978-3-319-26661-9 SP - 489 EP - 517 PB - Springer CY - Cham ER - TY - JOUR A1 - Schön, Torsten A1 - Stetter, Martin A1 - Tomé, Ana Maria A1 - Puntonet, Carlos Garcia A1 - Lang, Elmar W. T1 - Physarum Learner: A bio-inspired way of learning structure from data JF - Expert Systems with Applications UR - https://doi.org/10.1016/j.eswa.2014.03.002 Y1 - 2014 UR - https://doi.org/10.1016/j.eswa.2014.03.002 SN - 0957-4174 VL - 41 IS - 11 SP - 5353 EP - 5370 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Schön, Torsten A1 - Stetter, Martin A1 - Tomé, Ana Maria A1 - Lang, Elmar W. ED - Alvarez, Sergio A. ED - Solé-Casals, Jordi ED - Fred, Ana ED - Gamboa, Hugo T1 - A New Physarum Learner for Network Structure Learning from Biomedical Data T2 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing BIOSTEC N2 - A novel structure learning algorithm for Bayesian Networks based on a Physarum Learner is presented. The length of the connections within an initially fully connected Physarum-Maze is taken as the inverse Pearson correlation coefficient between the connected nodes. The Physarum Learner then estimates the shortest indirect paths between each pair of nodes. In each iteration, a score of the surviving edges is incremented. Finally, the highest scored connections are combined to form a Bayesian Network. The novel Physarum Learner method is evaluated with different configurations and compared to the LAGD Hill Climber showing comparable performance with respect to quality of training results and increased time efficiency for large data sets. UR - https://doi.org/10.5220/0004227401510156 Y1 - 2013 UR - https://doi.org/10.5220/0004227401510156 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48033 SN - 978-989-8565-36-5 SP - 151 EP - 156 PB - SciTePress CY - Setúbal ER -