Sökning: WFRF:(Sjöqvist Hugo 1992 )
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An Analysis of Fast...
An Analysis of Fast Learning Methods for Classifying Forest Cover Types
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- Sjöqvist, Hugo, 1992- (författare)
- Karolinska Institutet
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- Längkvist, Martin, 1983- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Department of Computer Science
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- Javed, Farrukh, 1984- (författare)
- Örebro universitet,Handelshögskolan vid Örebro Universitet,Department of Statistics
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(creator_code:org_t)
- 2020-06-04
- 2020
- Engelska.
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Ingår i: Applied Artificial Intelligence. - : Taylor & Francis Group. - 0883-9514 .- 1087-6545. ; 34:10, s. 691-709
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https://doi.org/10.1...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Proper mapping and classification of Forest cover types are integral in understanding the processes governing the interaction mechanism of the surface with the atmosphere. In the presence of massive satellite and aerial measurements, a proper manual categorization has become a tedious job. In this study, we implement three different modest machine learning classifiers along with three statistical feature selectors to classify different cover types from cartographic variables. Our results showed that, among the chosen classifiers, the standard Random Forest Classifier together with Principal Components performs exceptionally well, not only in overall assessment but across all seven categories. Our results are found to be significantly better than existing studies involving more complex Deep Learning models.
Ämnesord
- NATURVETENSKAP -- Annan naturvetenskap (hsv//swe)
- NATURAL SCIENCES -- Other Natural Sciences (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Nyckelord
- Statistik
- Statistics
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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