1 |
Y. Lecun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.
DOI
|
2 |
W. Yang, F. Zhou, R. Zhu, K. Fukui, G. Wang, and J. H. Xue, "Deep learning for image super-resolution," Neurocomputing, vol. 398, no. 10, pp. 291-292, 2020.
DOI
|
3 |
T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, "How many trees in a random forest?," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7376 LNAI, no. May, pp. 154-168, 2012.
|
4 |
M. Belgiu and L. Dragu, "Random forest in remote sensing: A review of applications and future directions," ISPRS J. Photogramm. Remote Sens., vol. 114, pp. 24-31, 2016.
DOI
|
5 |
A. Kamilaris and F. X. Prenafeta-Boldu, "Deep learning in agriculture: A survey," Comput. Electron. Agric., vol. 147, no. July 2017, pp. 70-90, 2018.
DOI
|
6 |
C. Shorten and T. M. Khoshgoftaar, "A survey on Image Data Augmentation for Deep Learning," J. Big Data, vol. 6, no. 1, 2019.
|
7 |
S. P. Mohanty, D. P. Hughes, and M. Salathe, "Using deep learning for image-based plant disease detection," Front. Plant Sci., vol. 7, no. September, pp. 1-10, 2016.
DOI
|
8 |
J. Wang and L. Perez, "The effectiveness of data augmentation in image classification using deep learning," arXiv, 2017.
|
9 |
G. Biau and E. Scornet, "A random forest guided tour," Test, vol. 25, no. 2, pp. 197-227, 2016.
DOI
|
10 |
T. R. Shaham, T. Dekel, and T. Michaeli, "SinGAN: Learning a generative model from a single natural image," Proc. IEEE Int. Conf. Comput. Vis., vol. 2019-Octob, pp. 4569-4579, 2019.
|
11 |
C. Strobl, A. L. Boulesteix, A. Zeileis, and T. Hothorn, "Bias in random forest variable importance measures: Illustrations, sources and a solution," BMC Bioinformatics, vol. 8, 2007.
|
12 |
T. Shi and S. Horvath, "Unsupervised learning with random forest predictors," J. Comput. Graph. Stat., vol. 15, no. 1, pp. 118-138, 2006.
DOI
|
13 |
J. S. Ham, Y. Chen, M. M. Crawford, and J. Ghosh, "Investigation of the random forest framework for classification of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 492-501, 2005.
DOI
|
14 |
Z. Meng, X. Guo, Z. Pan, D. Sun, and S. Liu, "Data Segmentation and Augmentation Methods Based on Raw Data Using Deep Neural Networks Approach for Rotating Machinery Fault Diagnosis," IEEE Access, vol. 7, pp. 79510-79522, 2019.
DOI
|
15 |
V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, "An assessment of the effectiveness of a random forest classifier for land-cover classification," ISPRS J. Photogramm. Remote Sens., vol. 67, no. 1, pp. 93-104, 2012.
DOI
|