Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Vincent Margot"'
Autor:
Harry Etienne, Pierre-Benoît Pagès, Jules Iquille, Pierre Emmanuel Falcoz, Laurent Brouchet, Jean-Philippe Berthet, Françoise Le Pimpec Barthes, Jacques Jougon, Marc Filaire, Jean-Marc Baste, Valentine Anne, Stéphane Renaud, Thomas D'Annoville, Jean Pierre Meunier, Christophe Jayle, Christian Dromer, Agathe Seguin-Givelet, Antoine Legras, Philippe Rinieri, Sophie Jaillard-Thery, Vincent Margot, Pascal-Alexandre Thomas, Marcel Dahan, Pierre Mordant
Publikováno v:
ERJ Open Research, Vol 10, Iss 1 (2024)
Introduction Non-small cell lung cancer (NSCLC) is often associated with compromised lung function. Real-world data on the impact of surgical approach in NSCLC patients with compromised lung function are still lacking. The objective of this study is
Externí odkaz:
https://doaj.org/article/09fbb8a9ab6e4785a088d1be5b101ff6
Autor:
Vincent Margot, George Luta
Publikováno v:
AI, Vol 2, Iss 4, Pp 621-635 (2021)
Interpretability is becoming increasingly important for predictive model analysis. Unfortunately, as remarked by many authors, there is still no consensus regarding this notion. The goal of this paper is to propose the definition of a score that allo
Externí odkaz:
https://doaj.org/article/2e9f39c6ea234a87b0cb6f6583f889ed
In this paper, we introduce a novel method to generate interpretable regression function estimators. The idea is based on called data-dependent coverings. The aim is to extract from the data a covering of the feature space instead of a partition. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa36be88eec150a3b4050723040b078a
https://hal.archives-ouvertes.fr/hal-02170687
https://hal.archives-ouvertes.fr/hal-02170687
Publikováno v:
The Seventh Public Investors Conference
The Seventh Public Investors Conference, Oct 2018, Rome, Italy
The Seventh Public Investors Conference, Oct 2018, Rome, Italy
International audience; We designed a machine learning algorithm that identifies patterns between ESG profiles and financial performances for companies in a large investment universe. The algorithm consists of regularly updated sets of rules that map
Publikováno v:
Machine Learning and Data Mining in Pattern Recognition ISBN: 9783319961323
MLDM (2)
MLDM (2)
RIPE is a novel deterministic and easily understandable prediction algorithm developed for continuous and discrete ordered data. It infers a model, from a sample, to predict and to explain a real variable Y given an input variable \(X \in \mathcal {X
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af451828e0f2e490ec8bfae7f3ba32de
https://doi.org/10.1007/978-3-319-96133-0_22
https://doi.org/10.1007/978-3-319-96133-0_22