Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Nicolas Serrette"'
Autor:
Thomas Lampert, Pierre Gançarski, Baptiste Lafabregue, Nicolas Serrette, Thi-Bich-Hanh Dao, Christel Vrain
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12 (11), pp.4606-4621. ⟨10.1109/JSTARS.2019.2950406⟩
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2019, 12 (11), pp.4606-4621. ⟨10.1109/JSTARS.2019.2950406⟩
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12 (11), pp.4606-4621. ⟨10.1109/JSTARS.2019.2950406⟩
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2019, 12 (11), pp.4606-4621. ⟨10.1109/JSTARS.2019.2950406⟩
International audience; The advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ign
Autor:
Germain Forestier, Christel Vrain, Nicolas Serrette, Baptiste Lafabregue, Thi-Bich-Hanh Dao, Pierre Gançarski, Bruno Crémilleux, Thomas Lampert
Publikováno v:
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery, 2018, 32 (6), pp.1663-1707. ⟨10.1007/s10618-018-0573-y⟩
Data Mining and Knowledge Discovery, Springer, 2018, 32 (6), pp.1663-1707. ⟨10.1007/s10618-018-0573-y⟩
Data Mining and Knowledge Discovery, 2018, 32 (6), pp.1663-1707. ⟨10.1007/s10618-018-0573-y⟩
Data Mining and Knowledge Discovery, Springer, 2018, 32 (6), pp.1663-1707. ⟨10.1007/s10618-018-0573-y⟩
International audience; Constrained clustering is becoming an increasingly popular approach in data mining. It offers a balance between the complexity of producing a formal definition of thematic classes-required by supervised methods-and unsupervise
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7daacee79c15b3142c0105e27f7b1e78
https://hal.science/hal-01831637/document
https://hal.science/hal-01831637/document