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Autor:
URQUHART, STEVEN
Publikováno v:
Studies in Canadian Literature / Études en Littérature Canadienne; 2022, Vol. 47 Issue 1, p285-304, 20p
Publikováno v:
PLoS Computational Biology, Vol 17, Iss 3, p e1008726 (2021)
We propose an analysis and applications of sample pooling to the epidemiologic monitoring of COVID-19. We first introduce a model of the RT-qPCR process used to test for the presence of virus in a sample and construct a statistical model for the vira
Externí odkaz:
https://doaj.org/article/918655a2c0ac432eb85ac6a2e6877c18
Autor:
Louis Devillaine, Raphaël Lambert, Jérôme Boutet, Saifeddine Aloui, Vincent Brault, Caroline Jolly, Etienne Labyt
Publikováno v:
Sensors, Vol 21, Iss 21, p 7026 (2021)
Five to ten percent of school-aged children display dysgraphia, a neuro-motor disorder that causes difficulties in handwriting, which becomes a handicap in the daily life of these children. Yet, the diagnosis of dysgraphia remains tedious, subjective
Externí odkaz:
https://doaj.org/article/f1560fcdb82746fcb17db41692fc6788
Autor:
Louis Deschamps, Louis Devillaine, Clement Gaffet, Raphaël Lambert, Saifeddine Aloui, Jérôme Boutet, Vincent Brault, Etienne Labyt, Caroline Jolly
Publikováno v:
Advances in Artificial Intelligence and Machine Learning
Advances in Artificial Intelligence and Machine Learning, In press, https://www.oajaiml.com/uploads/222/13194_pdf.pdf
Advances in Artificial Intelligence and Machine Learning, 2021, 1 (2), pp.114-135. ⟨10.54364/AAIML.2021.1108⟩
Advances in Artificial Intelligence and Machine Learning, In press, https://www.oajaiml.com/uploads/222/13194_pdf.pdf
Advances in Artificial Intelligence and Machine Learning, 2021, 1 (2), pp.114-135. ⟨10.54364/AAIML.2021.1108⟩
International audience; Dysgraphia is a writing disorder that affects a significant part of the population, especially school aged children and particularly boys. Nowadays, dysgraphia is insufficiently diagnosed, partly because of the cumbersomeness
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb0e93fa6cf0c1fa453999a756a6218f
https://doi.org/10.54364/aaiml.2021.1108
https://doi.org/10.54364/aaiml.2021.1108
Publikováno v:
Corporate IT Update (M2). 11/09/2009.
Publikováno v:
Electronic journal of statistics
Electronic journal of statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2020, 14 (1), pp.1234-1268. ⟨10.1214/20-EJS1695⟩
Electronic Journal of Statistics
Electronic Journal of Statistics, 2020, 14 (1), pp.1234-1268. ⟨10.1214/20-EJS1695⟩
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2020, 14 (1), pp.1234-1268. ⟨10.1214/20-EJS1695⟩
Electron. J. Statist. 14, no. 1 (2020), 1234-1268
Electronic journal of statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2020, 14 (1), pp.1234-1268. ⟨10.1214/20-EJS1695⟩
Electronic Journal of Statistics
Electronic Journal of Statistics, 2020, 14 (1), pp.1234-1268. ⟨10.1214/20-EJS1695⟩
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2020, 14 (1), pp.1234-1268. ⟨10.1214/20-EJS1695⟩
Electron. J. Statist. 14, no. 1 (2020), 1234-1268
The Latent Block Model (LBM) is a model-based method to cluster simultaneously the $d$ columns and $n$ rows of a data matrix. Parameter estimation in LBM is a difficult and multifaceted problem. Although various estimation strategies have been propos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b992aacde62470058503e9484cbfdd49
https://hal.archives-ouvertes.fr/hal-01511960v3/document
https://hal.archives-ouvertes.fr/hal-01511960v3/document
Publikováno v:
ESAIM: Proceedings and Surveys
ESAIM: Proceedings and Surveys, 2020, 68, pp.97-122. ⟨10.1051/proc/202068006⟩
ESAIM: Proceedings and Surveys, EDP Sciences, 2020, 68, pp.97-122. ⟨10.1051/proc/202068006⟩
ESAIM: Proceedings and Surveys, Vol 68, Pp 97-122 (2020)
ESAIM: Proceedings and Surveys, 2020, 68, pp.97-122. ⟨10.1051/proc/202068006⟩
ESAIM: Proceedings and Surveys, EDP Sciences, 2020, 68, pp.97-122. ⟨10.1051/proc/202068006⟩
ESAIM: Proceedings and Surveys, Vol 68, Pp 97-122 (2020)
International audience; Recent contributions to change-point detection, segmentation and inference for non-regular models are presented. Various problems are considered including the multiple change-point estimation with adaptive penalty for time ser
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::994643c5c225748097b0fbcab93eae47
https://hal.science/hal-02868510/file/proc206806.pdf
https://hal.science/hal-02868510/file/proc206806.pdf
Autor:
Vincent Brault, Charlotte Laclau
Publikováno v:
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery, Springer, 2019, 33 (2), pp.446-473. ⟨10.1007/s10618-018-0597-3⟩
Data Mining and Knowledge Discovery, 2019, 33 (2), pp.446-473. ⟨10.1007/s10618-018-0597-3⟩
Data Mining and Knowledge Discovery, Springer, 2019, 33 (2), pp.446-473. ⟨10.1007/s10618-018-0597-3⟩
Data Mining and Knowledge Discovery, 2019, 33 (2), pp.446-473. ⟨10.1007/s10618-018-0597-3⟩
International audience; Co-clustering is known to be a very powerful and efficient approach in unsupervised learning because of its ability to partition data based on both the observations and the variables of a given dataset. However, in high-dimens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::46195cd681f731430446b37db42ef8a0
https://doi.org/10.1007/s10618-018-0597-3
https://doi.org/10.1007/s10618-018-0597-3