Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Christel Dartigues-Pallez"'
Publikováno v:
Education Sciences, Vol 11, Iss 3, p 92 (2021)
Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to t
Externí odkaz:
https://doaj.org/article/b183a6f7bd2e41269e22931259b2c69e
Publikováno v:
Education Sciences
Volume 11
Issue 3
Education Sciences, Vol 11, Iss 92, p 92 (2021)
Volume 11
Issue 3
Education Sciences, Vol 11, Iss 92, p 92 (2021)
Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to t
Data Augmentation for Enlarging Student Feature Space and Improving Random Forest Success Prediction
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030782696
AIED (2)
AIED (2)
One of the main problems encountered when predicting student success, as a tool to aid students, is the lack of data used to model each student. This lack of data is due in part to the small number of students in each university course and also, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cfab04fbc3942247343731900fb97aef
https://doi.org/10.1007/978-3-030-78270-2_14
https://doi.org/10.1007/978-3-030-78270-2_14
Publikováno v:
BDMS 2020-7th Big Data Management and Service in DASFAA 2020
BDMS 2020-7th Big Data Management and Service in DASFAA 2020, Sep 2020, Jeju, South Korea
Lecture Notes in Computer Science ISBN: 9783030594121
DASFAA (Workshops)
BDMS 2020-7th Big Data Management and Service in DASFAA 2020, Sep 2020, Jeju, South Korea
Lecture Notes in Computer Science ISBN: 9783030594121
DASFAA (Workshops)
International audience; The research of Human Action Recognition (HAR) has made a lot of progress in recent years, and the research based on RGB images is the most extensive. However , there are two main shortcomings: the recognition accuracy is insu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::949889b3d57b31e032a46dfa852c04c6
https://hal.science/hal-02869941
https://hal.science/hal-02869941
Publikováno v:
Computational Linguistics and Intelligent Text Processing ISBN: 9783319754864
CICLing (2)
CICLing (2)
To overcome short text classification issues due to shortness and sparseness, the enrichment process is classically proposed: topics (word clusters) are extracted from external knowledge sources using Latent Dirichlet Allocation. All the words, assoc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cea58c0f348c315d2acc7c0a05252101
https://doi.org/10.1007/978-3-319-75487-1_34
https://doi.org/10.1007/978-3-319-75487-1_34
Publikováno v:
MobiQuitous
13th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
13th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Nov 2016, Hiroshima, Japan. Proceeding of the 13th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
13th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
13th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Nov 2016, Hiroshima, Japan. Proceeding of the 13th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
International audience; A lot of research has been done for human activity recognition. But most of it uses a static and immutable set of sensors known beforehand. This approach does not work when applied to a ubiquitous or mobile system, since we ca
Publikováno v:
SAC
We propose a method to improve the performance of Random Forests for classifying short texts interactively. In short text classification, the principle of learning algorithms is to build a static model using a training dataset, then to use this model
Autor:
Ameni Bouaziz, Frédéric Precioso, Patrick Lloret, Christel Dartigues-Pallez, Célia da Costa Pereira
Publikováno v:
Data Warehousing and Knowledge Discovery
Data Warehousing and Knowledge Discovery, Sep 2014, Munich, Germany. ⟨10.1007/978-3-319-10160-6_26⟩
Data Warehousing and Knowledge Discovery ISBN: 9783319101590
DaWaK
Data Warehousing and Knowledge Discovery, Sep 2014, Munich, Germany. ⟨10.1007/978-3-319-10160-6_26⟩
Data Warehousing and Knowledge Discovery ISBN: 9783319101590
DaWaK
International audience; Using traditional Random Forests in short text classification revealed a performance degradation compared to using them for standard texts. Shortness, sparseness and lack of contextual information in short texts are the reason
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::700c623e8878c368f634190903c9db83
https://hal.archives-ouvertes.fr/hal-01325212
https://hal.archives-ouvertes.fr/hal-01325212
Autor:
Denis Pallez, Andrea G. B. Tettamanzi, Philippe Gourbesville, Christel Dartigues-Pallez, Célia da Costa Pereira
Publikováno v:
Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation
Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 2011, New York, NY, USA, United States. pp.1715--1722, ⟨10.1145/2001576.2001807⟩
GECCO
Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 2011, New York, NY, USA, United States. pp.1715--1722, ⟨10.1145/2001576.2001807⟩
GECCO
We propose a data-driven evolutionary approach to the modeling of marine currents in the Bay of Monaco. The CMA (Covariance Matrix Adaptation) evolution strategy is used to optimize the parameters of a predictive model that may be used as a surrogate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6215e2144c17617320f19353c3b51df
https://hal.univ-cotedazur.fr/hal-01322773
https://hal.univ-cotedazur.fr/hal-01322773