Consensus methods based on machine learning techniques for marine phytoplankton presence–absence prediction-Reference-Cited by-同舟云学术

Consensus methods based on machine learning techniques for marine phytoplankton presence–absence prediction

Published:2017-11 Issue: Volume:42 Page:46-54
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Bourel M.^ORCID,Crisci C.,Martínez A.

Funder

ECOS-Sud Aprendizaje Automático para la Modelización y el Análisis de Recursos Naturales

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Ecological Modeling,Modeling and Simulation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference74 articles.

1. Combining independent and unbiased classifiers using weighted average;Alexandre,2000

2. Estimating Cell Numbers;Andersen,2003

3. Predicting potentially toxigenic pseudo-Nitzschia blooms in the chesapeake Bay;Anderson;J. Mar. Syst.,2010

4. Ensemble forecasting of species distributions;Araújo;Trends Ecol. Evol.,2007

5. Trophic niche shifts driven by phytoplankton in sandy beach ecosystems;Bergamino;Estuar. Coast. Shelf Sci.,2016

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