Zobrazeno 1 - 10
of 3 043
pro vyhledávání: '"J. Ostermann"'
Autor:
D. Gritzner, J. Ostermann
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 483-492 (2020)
Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which do not generalize well to new
Externí odkaz:
https://doaj.org/article/3ded3e4daff743f7b073f169ae8c40a6
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2, Pp 845-852 (2018)
In this paper we deal with the problem of measuring the similarity between training and tests datasets in the context of transfer learning (TL) for image classification. TL tries to transfer knowledge from a source domain, where labelled training sam
Externí odkaz:
https://doaj.org/article/f98f0f28bcec4fdc932217a27473a727
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-1-W1, Pp 229-236 (2017)
Supervised machine learning needs high quality, densely sampled and labelled training data. Transfer learning (TL) techniques have been devised to reduce this dependency by adapting classifiers trained on different, but related, (source) training da
Externí odkaz:
https://doaj.org/article/cb88d6f6676a40f5b23a2a629bfe2a42
Publikováno v:
64. Kongress der Deutschen Gesellschaft für Gynäkologie und Geburtshilfe e. V..
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1bddc616e5e17478589ef2283c17425f
https://doi.org/10.1055/s-0042-1756979
https://doi.org/10.1055/s-0042-1756979
Publikováno v:
Value in Health. 25:S2-S3
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee28f27e46b2a454a69504b3f24e11b8
https://doi.org/10.1016/j.jval.2022.09.020
https://doi.org/10.1016/j.jval.2022.09.020
Autor:
J. Ostermann, M. Smollich
Publikováno v:
Aktuelle Ernährungsmedizin.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3328019bbccbdc2f2d4a78bf8f74faa0
https://doi.org/10.1055/s-0042-1748286
https://doi.org/10.1055/s-0042-1748286
Publikováno v:
Journal of Medical Economics, 25(1), 481-490. Routledge/Taylor & Francis Group
BACKGROUND: South Africa (SA) has the world's highest burden of HIV infection, with an estimated 13.7% of the population living with HIV (PLWH/Persons Living With HIV). The early identification of PLWH and rapid engagement of them in HIV treatment ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30cadc699c130669c8b7623c6e4c4503
https://cris.maastrichtuniversity.nl/en/publications/f7f9da89-db7c-4282-bbf5-0d2641db4f7f
https://cris.maastrichtuniversity.nl/en/publications/f7f9da89-db7c-4282-bbf5-0d2641db4f7f
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol I-4, Pp 233-238 (2012)
Data in Geo Information Systems (GIS) is used for map services and various applications. Thus, quality assessment on a regular basis is required to keep the data up-to-date. In this paper we focus on one key reasons for updates: incorrect object bord
Externí odkaz:
https://doaj.org/article/6bf536c5046f4c11b59a0e242ead0046
Autor:
T. Büschenfeld, J. Ostermann
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol I-7, Pp 117-122 (2012)
In this paper, we present a method for automatic refinement of training data. Many classifiers from machine learning used in applications in the remote sensing domain, rely on previously labelled training data. This labelling is often done by human o
Externí odkaz:
https://doaj.org/article/2beeafc2fbcc47fbb6d4c4035ebf79c3
Autor:
C. L. Gray, B. W. Pence, J. Ostermann, R. A. Whetten, K. O'Donnell, N. M. Thielman, K. Whetten
Publikováno v:
Cambridge Prisms: Global Mental Health, Vol 2 (2015)
Background. Approximately 153 million children worldwide are orphaned and vulnerable to potentially traumatic events (PTEs). Gender differences in PTEs in low- and middle-income countries (LMIC) are not well-understood, although support services and
Externí odkaz:
https://doaj.org/article/8a503f49560f46eebf9f4656fc47ebea