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Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers
Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said...
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| Publicado no: | PLoS One |
|---|---|
| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5756090/ https://ncbi.nlm.nih.gov/pubmed/29304512 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0188996 |
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