Zobrazeno 1 - 10
of 1 087
pro vyhledávání: '"Random Ferns"'
Autor:
Wei, Pengchao, Hänsch, Ronny
Random Ferns -- as a less known example of Ensemble Learning -- have been successfully applied in many Computer Vision applications ranging from keypoint matching to object detection. This paper extends the Random Fern framework to the semantic segme
Externí odkaz:
http://arxiv.org/abs/2202.03498
Autor:
AYDOĞAN CULHA, Ülger1 ulgeraydogan@gmail.com, KARABULUT, Erdem2
Publikováno v:
Turkiye Klinikleri Journal of Biostatistics. 2022, Vol. 14 Issue 2, p81-89. 9p.
Autor:
Sangwon Kim, Byoung Chul Ko
Publikováno v:
IEEE Access, Vol 8, Pp 8533-8542 (2020)
Randomized sampling-based ensemble learning is emerging as a new alternative to deep neural networks (DNNs) because it supports diversity and locality and does not require backpropagation in the learning process. By connecting randomized weak classif
Externí odkaz:
https://doaj.org/article/555d2f99acc2496396ed7b4394ca89ee
Akademický článek
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Autor:
Kursa, Miron Bartosz
Many machine learning methods can produce variable importance scores expressing the usability of each feature in context of the produced model; those scores on their own are yet not sufficient to generate feature selection, especially when an all rel
Externí odkaz:
http://arxiv.org/abs/1604.06133
Autor:
Browet, Arnaud, De Vleeschouwer, Christophe, Jacques, Laurent, Mathiah, Navrita, Saykali, Bechara, Migeotte, Isabelle
The progress in imaging techniques have allowed the study of various aspect of cellular mechanisms. To isolate individual cells in live imaging data, we introduce an elegant image segmentation framework that effectively extracts cell boundaries, even
Externí odkaz:
http://arxiv.org/abs/1602.05439
Autor:
Li, Yang1 (AUTHOR), Wang, Zheng2 (AUTHOR), You, Zhu-Hong3 (AUTHOR), Li, Li-Ping4 (AUTHOR), Hu, Xuegang1 (AUTHOR)
Publikováno v:
Computational & Mathematical Methods in Medicine. 2/22/2022, p1-11. 11p.
Autor:
Kursa, Miron B.
Publikováno v:
Journal of Statistical Software, 61 (10), pp. 1 - 13 (2014)
In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns. It differs from the original by the ability of consuming categorical and numerical attributes instead of only binary ones. Also, inste
Externí odkaz:
http://arxiv.org/abs/1202.1121
Akademický článek
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Autor:
Zhang, Miaohui1 zhmh@henu.edu.cn, Xin, Ming2
Publikováno v:
PLoS ONE. 9/9/2016, Vol. 11 Issue 9, p1-13. 13p.