Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Jacopo Acquarelli"'
Autor:
Jacopo Acquarelli, Twan van Laarhoven, Geert J Postma, Jeroen J Jansen, Anne Rijpma, Sjaak van Asten, Arend Heerschap, Lutgarde M C Buydens, Elena Marchiori
Publikováno v:
PLoS ONE, Vol 17, Iss 8, p e0268881 (2022)
PurposeTo evaluate the value of convolutional neural network (CNN) in the diagnosis of human brain tumor or Alzheimer's disease by MR spectroscopic imaging (MRSI) and to compare its Matthews correlation coefficient (MCC) score against that of other m
Externí odkaz:
https://doaj.org/article/670732cba2a14820b818ac418a082083
Publikováno v:
Remote Sensing, Vol 10, Iss 7, p 1156 (2018)
Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of t
Externí odkaz:
https://doaj.org/article/483943928e6b4232a06f2bcb20d1ddec
Autor:
Elena Marchiori, Thanh N. Tran, Jacopo Acquarelli, Twan van Laarhoven, Lutgarde M. C. Buydens
Publikováno v:
Remote Sensing, Vol 10, Iss 7, p 1156 (2018)
Remote Sensing; Volume 10; Issue 7; Pages: 1156
Remote Sensing, 10, 1-19
Acquarelli, J, Marchiori, E, Buydens, L M C, Thanh Tran, & van Laarhoven, T 2018, ' Spectral-Spatial Classification of Hyperspectral Images : Three Tricks and a New Learning Setting ', Remote Sensing, vol. 10, no. 7, 1156 . https://doi.org/10.3390/rs10071156
Remote Sensing, 10(7):1156. MDPI
Remote Sensing, 10, 7, pp. 1-19
Remote Sensing; Volume 10; Issue 7; Pages: 1156
Remote Sensing, 10, 1-19
Acquarelli, J, Marchiori, E, Buydens, L M C, Thanh Tran, & van Laarhoven, T 2018, ' Spectral-Spatial Classification of Hyperspectral Images : Three Tricks and a New Learning Setting ', Remote Sensing, vol. 10, no. 7, 1156 . https://doi.org/10.3390/rs10071156
Remote Sensing, 10(7):1156. MDPI
Remote Sensing, 10, 7, pp. 1-19
Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of t
Publikováno v:
Applications of Evolutionary Computation ISBN: 9783319312033
EvoApplications (1)
EvoApplications (1)
Constrained non-negative matrix factorization (CNMF) is an effective machine learning technique to cluster documents in the presence of class label constraints. In this work, we provide a novel application of this technique in research on neuro-degen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::955ad93e441c26a8d5f15a7ee0b2080d
http://hdl.handle.net/11365/1006623
http://hdl.handle.net/11365/1006623