Using comprehensive machine‐learning models to classify complex morphological characters

1. Recognizing and classifying multiple morphological features, such as patterns, sizes, and textures, can provide a comprehensive understanding of their variability and phenotypic evolution. Yet, quantitatively measuring complex morphological characters remains challenging. 2. We provide a machine...

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Detalles Bibliográficos
Autores principales: Teng, Dequn, Li, Fengyuan, Zhang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8328437/
https://www.ncbi.nlm.nih.gov/pubmed/34367585
http://dx.doi.org/10.1002/ece3.7845

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8328437/
https://www.ncbi.nlm.nih.gov/pubmed/34367585
http://dx.doi.org/10.1002/ece3.7845

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