Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Nadine Hilgert"'
Autor:
Rémi Mahmoud, Pierre Casadebaig, Nadine Hilgert, Lionel Alletto, Grégoire T. Freschet, Claire de Mazancourt, Noémie Gaudio
Publikováno v:
Agronomy for Sustainable Development. 42
Publikováno v:
SSRN Electronic Journal.
Autor:
François Tardieu, Claude Welcker, Santiago Alvarez Prado, Llorenç Cabrera-Bosquet, Isabelle Sanchez, Antonin Grau, Nadine Hilgert
Publikováno v:
Journal of Experimental Botany
Journal of Experimental Botany, Oxford University Press (OUP), 2019, 70 (15), pp.3693-3698. ⟨10.1093/jxb/erz191⟩
Journal of Experimental Botany, 2019, 70 (15), pp.3693-3698. ⟨10.1093/jxb/erz191⟩
Journal of Experimental Botany 15 (70), 3693-3698. (2019)
Journal of Experimental Botany, Oxford University Press (OUP), 2019, 70 (15), pp.3693-3698. ⟨10.1093/jxb/erz191⟩
Journal of Experimental Botany, 2019, 70 (15), pp.3693-3698. ⟨10.1093/jxb/erz191⟩
Journal of Experimental Botany 15 (70), 3693-3698. (2019)
Excluding outlier plants (biological replicates deviating from the expected distribution on a multi-criteria basis) from phenotypic datasets is necessary to avoid false-positive associations between genome markers and traits.
Based on case studi
Based on case studi
Publikováno v:
ECML PKDD 2020-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
ECML PKDD 2020-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent / Virtual, Belgium
ECML PKDD 2020-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent / Virtual, Belgium. ⟨10.1007/978-3-030-67670-4_34⟩
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track ISBN: 9783030676698
ECML/PKDD (5)
ECML PKDD 2020-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent / Virtual, Belgium
ECML PKDD 2020-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent / Virtual, Belgium. ⟨10.1007/978-3-030-67670-4_34⟩
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track ISBN: 9783030676698
ECML/PKDD (5)
International audience; Dirichlet Process Mixture (DPM) is a model used for multivariate clustering with the advantage of discovering the number of clusters automatically and offering favorable characteristics, but with prohibitive response times, wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65788f4b89b262b22fe1df300d7e904d
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03036910/file/ECML_PKDD_2020.pdf
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03036910/file/ECML_PKDD_2020.pdf
Publikováno v:
IEEE International Conference on Big Data
IEEE Big Data 2019-IEEE International Conference on Big Data
IEEE Big Data 2019-IEEE International Conference on Big Data, Dec 2019, Los-Angeles, United States
IEEE BigData
IEEE Big Data 2019-IEEE International Conference on Big Data
IEEE Big Data 2019-IEEE International Conference on Big Data, Dec 2019, Los-Angeles, United States
IEEE BigData
International audience; Clustering is a data mining technique intensively used for data analytics, with applications to marketing, security, text/document analysis, or sciences like biology, astronomy, and many more. Dirichlet Process Mixture (DPM) i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61871bd307520f6cf915cf190c390293
https://hal-lirmm.ccsd.cnrs.fr/lirmm-02364411/document
https://hal-lirmm.ccsd.cnrs.fr/lirmm-02364411/document
Autor:
François Tardieu, Romain Chapuis, Vincent Negre, Jonathan Mineau-Cesari, Brigitte Charnomordic, Nadine Hilgert, Isabelle Sanchez, Pascal Neveu, Anne Tireau, Nicolas Brichet, Cyril Pommier, Llorenç Cabrera-Bosquet
Publikováno v:
New Phytologist
New Phytologist, Wiley, 2019, 221 (1), pp.588-601. ⟨10.1111/nph.15385⟩
New Phytologist 1 (221), 588-601. (2019)
The New Phytologist
New Phytologist, Wiley, 2019, 221 (1), pp.588-601. ⟨10.1111/nph.15385⟩
New Phytologist 1 (221), 588-601. (2019)
The New Phytologist
International audience; Summary : . Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events. . The open-source Phenotyping Hybrid Informati
Publikováno v:
Electronic Journal of Statistics 1 (12), 985-1018. (2018)
Electronic Journal of Statistics
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (1), pp.985-1018. ⟨10.1214/18-EJS1412⟩
Electron. J. Statist. 12, no. 1 (2018), 985-1018
Electronic journal of statistics
Electronic journal of statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (1), pp.985-1018. ⟨10.1214/18-EJS1412⟩
Electronic Journal of Statistics
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (1), pp.985-1018. ⟨10.1214/18-EJS1412⟩
Electron. J. Statist. 12, no. 1 (2018), 985-1018
Electronic journal of statistics
Electronic journal of statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (1), pp.985-1018. ⟨10.1214/18-EJS1412⟩
International audience; The aim of this paper is to propose estimators of the unknown functional coefficients in the Functional Concurrent Model (FCM). We extend the Ridge Regression method developed in the classical linear case to the functional dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be6c4e649552e484f2f7b7f49088af02
http://prodinra.inra.fr/record/440893
http://prodinra.inra.fr/record/440893
Publikováno v:
Statistics and Probability Letters
Statistics and Probability Letters, Elsevier, 2016, 113, pp.7-15. ⟨10.1016/j.spl.2016.02.006⟩
Statistics and Probability Letters (113), 7-15. (2016)
Statistics and Probability Letters, Elsevier, 2016, 113, pp.7-15. ⟨10.1016/j.spl.2016.02.006⟩
Statistics and Probability Letters (113), 7-15. (2016)
This work deals with the estimation of the noise in functional linear regression when both the response and the covariate are functional. Namely, we propose two estimators of the covariance operator of the noise. We give some asymptotic properties of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ab01b3a7c056d0beeb4e0704d90f90c
https://hal.archives-ouvertes.fr/hal-01331242
https://hal.archives-ouvertes.fr/hal-01331242
Publikováno v:
Statistical Methodology
Statistical Methodology, Elsevier, 2016, 33, pp.96-113. ⟨10.1016/j.stamet.2016.08.003⟩
Statistical Methodology (33), 96–113. (2016)
Statistical Methodology, Elsevier, 2016, 33, pp.96-113. ⟨10.1016/j.stamet.2016.08.003⟩
Statistical Methodology (33), 96–113. (2016)
A new statistical approach for on-line change detection in uncertain dynamic system is proposed. In change detection problem, the distribution of a sequence of observations can change at some unknown instant. The goal is to detect this change, for ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c2db382ba1d4c1e624edb25932d5594
https://hal.archives-ouvertes.fr/hal-01581265
https://hal.archives-ouvertes.fr/hal-01581265
Publikováno v:
Computational Statistics and Data Analysis
Computational Statistics and Data Analysis, Elsevier, 2008, 52 (9), pp.4161-4174. ⟨10.1016/j.csda.2008.01.026⟩
Computational Statistics and Data Analysis, Elsevier, 2008, 52 (9), pp.4161-4174. ⟨10.1016/j.csda.2008.01.026⟩
aeres : ACL; International audience; Statistical methods dealing with change detection and isolation in dynamical systems are based on algorithms deriving from hypothesis testing. As for any statistical test, the problem of threshold choice has to be