Genetic Programming Bibliography entries for Daniel Beck

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GP coauthors/coeditors: Yolanda S Baker, Rajeev Agrawal, James A Foster, Gerry Dozier,

Genetic Programming Articles by Daniel Beck

• Daniel Beck and James A. Foster. Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis. BioData Mining, 8(23) 2015. details

• Daniel Beck and James A. Foster. Machine Learning Techniques Accurately Classify Microbial Communities by Bacterial Vaginosis Characteristics. PLoS ONE, 9(2):e87830, 2014. details

Genetic Programming PhD doctoral thesis Daniel Beck

• Daniel Beck. Investigating the use of classification models to study microbial community associations with bacterial vaginosis. PhD thesis, University of Idaho, USA, 2014. details

Genetic Programming conference papers by Daniel Beck

• Yolanda S. Baker and Rajeev Agrawal and James A. Foster and Daniel Beck and Gerry Dozier. Applying machine learning techniques in detecting Bacterial Vaginosis. In 2014 International Conference on Machine Learning and Cybernetics, volume 1, pages 241-246, Lanzhou, China, 2014. IEEE. details

• Yolanda S. Baker and Rajeev Agrawal and James A. Foster and Daniel Beck and Gerry Dozier. Detecting Bacterial Vaginosis Using Machine Learning. In Proceedings of the 2014 ACM Southeast Regional Conference, pages 46:1-46:4, Kennesaw, Georgia, USA, 2014. ACM. details