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| Registros recuperados: 25 | |
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| Martinez, Elodie; Gorgues, Thomas; Lengaigne, Matthieu; Fontana, Clement; Sauzède, Raphaëlle; Menkes, Christophe; Uitz, Julia; Di Lorenzo, Emanuele; Fablet, Ronan. |
| Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability... |
| Tipo: Text |
Palavras-chave: Machine learning; Phytoplankton variability; Satellite ocean color; Decadel variability; Global scale. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00641/75314/75810.pdf |
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| Pilaszy, Istvan; Dobrowiecki, Tadeusz. |
| In this paper we presented a lot of experiments that examine how the particular parts of the documents do contribute to the performance of a classifier. We evaluated text classifiers on two very different text corpora. We conclude that some parts of the text are more important from the point of text classification performance. Giving higher weights to more important parts can increase the performance of the classifier. The question, that which parts are more or less important depends on the nature of the documents in the corpora. Some tasks that remains to be done: − More text corpora should be investigated. − In section 6.4 we optimized the number of features to be kept independent from the section. However, it could be optimized for each section. −... |
| Tipo: Journal Article |
Palavras-chave: Machine learning; Text categorization; Classifier ensembles; Research and Development/Tech Change/Emerging Technologies. |
| Ano: 2007 |
URL: http://purl.umn.edu/58927 |
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| Pereira,Tonismar dos S.; Robaina,Adroaldo D.; Peiter,Marcia X.; Torres,Rogerio R.; Bruning,Jhosefe. |
| ABSTRACT The aim of this study was to present and to evaluate methodologies for the estimation of soil resistance to penetration (RP) using artificial intelligence prediction techniques. In order to do so, a data base with values of physical-water characteristics of the soils available in the literature was used, and the performances of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were evaluated. The models generated from the ANNs were implemented through the multilayer perceptron with backpropagation algorithm of Matlab software, varying the number of neurons in the input and intermediate layers. For the procedure from SVM, the RapidMiner software was used, varying input variables, the kernel function and the coefficients of these... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil compaction; Machine learning; Support vector machines; Artificial neural networks. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000100142 |
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| Rosso, Isabella; Mazloff, Matthew R.; Talley, Lynne D.; Purkey, Sarah G.; Freeman, Natalie M.; Maze, Guillaume. |
| The Southern Ocean (SO) is one of the most energetic regions in the world, where strong air‐sea fluxes, oceanic instabilities, and flow‐topography interactions yield complex dynamics. The Kerguelen Plateau (KP) region in the Indian sector of the SO is a hotspot for these energetic dynamics, which result in large spatio‐temporal variability of physical and biogeochemical (BGC) properties throughout the water column. Data from Argo floats (including biogeochemical) are used to investigate the spatial variability of intermediate and deep water physical and BGC properties. An unsupervised machine learning classification approach is used to organize the float profiles into five SO frontal zones based on their temperature and salinity structure between 300 and... |
| Tipo: Text |
Palavras-chave: Southern Ocean; Kerguelen Plateau; Argo; Unsupervised clustering; Machine learning. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00613/72471/71438.pdf |
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| Registros recuperados: 25 | |
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