other · Figshare · 2015
Detection performance and efficiency.
Daniel P. Zitterbart (219647), Lars Kindermann (444565), Elke Burkhardt (444566), Olaf Boebel (444567)
Cited by 0Open access
Abstract
<p>A: Cue-based classifier ROC curves for training, testing and two (day and night) validation datasets. AUC values: Training: 0.99; Testing: 0.98; Validation day: 0.90; Validation night: 0.98<b>.</b> B: Lines with error bars: Proportion of successful automatic detections of all visually detected blows (black), and proportion of visually detected blows of all automatic detections (grey) versus distance (bin width of 1 km). Errorbars give the standard error. Bar plot: Number of automatic (black) and visual (grey) detections versus distance.</p>
Cite this paper
(219647), D. P. Z., (444565), L. K., (444566), E. B., & (444567), O. B. (2015). Detection performance and efficiency. Figshare. https://doi.org/10.1371/journal.pone.0071217.g003
(219647), Daniel P. Zitterbart, et al. “Detection performance and efficiency.” Figshare, 2015, https://doi.org/10.1371/journal.pone.0071217.g003.
(219647), Daniel P. Zitterbart, Lars Kindermann (444565), Elke Burkhardt (444566), and Olaf Boebel (444567). 2015. “Detection performance and efficiency.” Figshare. https://doi.org/10.1371/journal.pone.0071217.g003.
(219647), D.P.Z. et al. (2015) ‘Detection performance and efficiency’, Figshare. Available at: https://doi.org/10.1371/journal.pone.0071217.g003.
@misc{2196472015detection,
title = {{Detection performance and efficiency.}},
author = {(219647), Daniel P. Zitterbart and (444565), Lars Kindermann and (444566), Elke Burkhardt and (444567), Olaf Boebel},
journal = {Figshare},
year = {2015},
doi = {10.1371/journal.pone.0071217.g003}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.