Prediction of fishing effort distributions using boosted regression trees.
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Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data SetsIntra-and-inter species biomass prediction in a plantation forest: testing the utility of high spatial resolution spaceborne multispectral RapidEye sensor and advanced machine learning algorithmsFit to predict? Ecoinformatics for predicting the catchability of a pelagic fish in near real-time.Fleet behavior is responsive to a large-scale environmental disturbance: Hypoxia effects on the spatial dynamics of the northern Gulf of Mexico shrimp fishery.Comparing pseudo-absences generation techniques in Boosted Regression Trees models for conservation purposes: A case study on amphibians in a protected area.The environmental niche of the global high seas pelagic longline fleet
P2860
Prediction of fishing effort distributions using boosted regression trees.
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name
Prediction of fishing effort distributions using boosted regression trees.
@en
Prediction of fishing effort distributions using boosted regression trees.
@nl
type
label
Prediction of fishing effort distributions using boosted regression trees.
@en
Prediction of fishing effort distributions using boosted regression trees.
@nl
prefLabel
Prediction of fishing effort distributions using boosted regression trees.
@en
Prediction of fishing effort distributions using boosted regression trees.
@nl
P2093
P2860
P356
P1476
Prediction of fishing effort distributions using boosted regression trees.
@en
P2093
Candan U Soykan
Heidi Dewar
Suzanne Kohin
Tomoharu Eguchi
P2860
P356
10.1890/12-0826.1
P577
2014-01-01T00:00:00Z