Outputs (3)

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers (2018)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. In 2018 IEEE Congress on Evolutionary Computation (CEC) : 8-13 July 2018, Rio de Janeiro, Brazil ; proceedings (646-653). https://doi.org/10.1109/cec.2018.8477779

This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to e... Read More about Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers.

Computing Based on Material Training: Application to Binary Classification Problems (2017)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., & Petty, M. (2017). Computing Based on Material Training: Application to Binary Classification Problems. In 2017 IEEE International Conference on Rebooting Computing (ICRC) : 8-9 November 2017, Washington, DC, USA ; proceedings (274-281). https://doi.org/10.1109/icrc.2017.8123677

Evolution-in-materio is a form of unconventional computing combining materials' training and evolutionary search algorithms. In previous work, a mixture of single-walled-carbon-nanotubes (SWCNTs) dispersed in a liquid crystal (LC) was trained so that... Read More about Computing Based on Material Training: Application to Binary Classification Problems.

Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms (2016)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Massey, M., Zeze, D., Pearson, C., Groves, C., & Petty, M. (2016). Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms. In M. Amos, & A. Condon (Eds.), Unconventional computation and natural computation : 15th International Conference, UCNC 2016, Manchester, UK, July 11-15, 2016 ; proceedings (130-141). https://doi.org/10.1007/978-3-319-41312-9_11

Evolution-in-Materio uses evolutionary algorithms (EA) to exploit the physical properties of unconfigured, physically rich materials, in effect transforming them into information processors. The potential of this technique for machine learning proble... Read More about Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms.