The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 12 of 50
Back to Result List

A New Physarum Learner for Network Structure Learning from Biomedical Data

  • 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.

Download full text files

Export metadata

Additional Services

Search Google Scholar
Metadaten
Author:Torsten SchönORCiD, Martin Stetter, Ana Maria Tomé, Elmar W. Lang
Language:English
Document Type:Conference Paper
Conference:BIOSIGNALS 2013 - International Conference on Bio-inspired Systems and Signal Processing, Barcelona (Spain), 11.-14.02.2013
Year of first Publication:2013
published in (English):Proceedings of the International Conference on Bio-inspired Systems and Signal Processing BIOSTEC
Editor(s):Sergio A. Alvarez, Jordi Solé-Casals, Ana Fred, Hugo Gamboa
Publisher:SciTePress
Place of publication:Setúbal
ISBN:978-989-8565-36-5
First Page:151
Last Page:156
Review:peer-review
Open Access:ja
URN:urn:nbn:de:bvb:573-48033
Related Identifier:https://doi.org/10.5220/0004227401510156
Licence (German):License Logo Creative Commons BY-NC-ND 4.0
Release Date:2024/07/01