Emmanuele Peluso

Orcid: 0000-0002-6829-2180

According to our database1, Emmanuele Peluso authored at least 13 papers between 2015 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2023
Information theoretic and neural computational tools for meta-analysis of cumulative databases in the age of Big Physics experiments.
Neural Comput. Appl., 2023

Upgrades of Genetic Programming for Data-Driven Modeling of Time Series.
Evol. Comput., 2023

2022
A systemic approach to classification for knowledge discovery with applications to the identification of boundary equations in complex systems.
Artif. Intell. Rev., 2022

2020
A Refinement of Recurrence Analysis to Determine the Time Delay of Causality in Presence of External Perturbations.
Entropy, 2020

2019
On the Use of Entropy to Improve Model Selection Criteria.
Entropy, 2019

Geodesic Distance on Gaussian Manifolds to Reduce the Statistical Errors in the Investigation of Complex Systems.
Complex., 2019

2018
On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals.
Entropy, 2018

A New Approach to Bolometric Tomography in Tokamaks.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2018

2017
Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds.
Entropy, 2017

Deriving Realistic Mathematical Models from Support Vector Machines for Scientific Applications.
Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2017

Complex networks for the analysis of the synchronization of time series relevant for plasma fusion diagnostics.
Proceedings of the 2017 European Conference on Circuit Theory and Design, 2017

2016
A Metric to Improve the Robustness of Conformal Predictors in the Presence of Error Bars.
Proceedings of the Conformal and Probabilistic Prediction with Applications, 2016

2015
How to Handle Error Bars in Symbolic Regression for Data Mining in Scientific Applications.
Proceedings of the Statistical Learning and Data Sciences - Third International Symposium, 2015


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