R. Bellman, DYNAMIC PROGRAMMING AND LAGRANGE MULTIPLIERS, Proceedings of the National Academy of Sciences, vol.42, issue.10, pp.767-769, 1956.
DOI : 10.1073/pnas.42.10.767

URL : http://doi.org/10.1073/pnas.42.10.767

C. Bentes, D. Velotto, and B. Tings, Ship Classification in TerraSAR-X Images With Convolutional Neural Networks, IEEE Journal of Oceanic Engineering, vol.43, issue.1, 2017.
DOI : 10.1109/JOE.2017.2767106

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401

URL : http://www.svms.org/training/BOGV92.pdf

L. Breiman, Random forests. Machine learning, pp.5-32, 2001.

K. Denos, M. Ravaut, A. Fagette, and H. Lim, Deep learning applied to underwater mine warfare, OCEANS 2017, Aberdeen, pp.1-7, 2017.
DOI : 10.1109/OCEANSE.2017.8084910

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang et al., Decaf: A deep convolutional activation feature for generic visual recognition, International conference on machine learning, pp.647-655, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016.
DOI : 10.1109/CVPR.2016.90

URL : http://arxiv.org/pdf/1512.03385

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed et al., Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, IEEE Signal Processing Magazine, vol.29, issue.6, pp.2982-97, 2012.
DOI : 10.1109/MSP.2012.2205597

P. Johnston and M. Poole, Marine surveillance capabilities of the AutoNaut wave-propelled unmanned surface vessel (USV), OCEANS 2017, Aberdeen, pp.1-46, 2017.
DOI : 10.1109/OCEANSE.2017.8084782

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Communications of the ACM, vol.60, issue.6
DOI : 10.1162/neco.2009.10-08-881

URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

H. Li, K. Yang, H. Xia, and Q. Yang, Model-independent depth classification of transient acoustic signal in deep water, OCEANS 2017, Aberdeen, pp.1-4, 2017.
DOI : 10.1109/OCEANSE.2017.8084591

J. Lossent, C. Gervaise, and L. D. Iorio, Cartographie de la biophonie desécosystèmesdes´desécosystèmes côtiers, pp.1-5, 2015.

M. Malfante, M. Dalla-mura, J. I. Mars, and C. Gervaise, Automatic fish sounds classification, The Journal of the Acoustical Society of America, vol.139, issue.4, pp.2115-2116, 2016.
DOI : 10.1121/1.4950295

URL : https://hal.archives-ouvertes.fr/hal-01343780

M. Malfante, M. Dalla-mura, J. Métaxian, J. I. Mars, O. Macedo et al., Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives, IEEE Signal Processing Magazine, vol.35, issue.2, 2017.
DOI : 10.1109/MSP.2017.2779166

URL : https://hal.archives-ouvertes.fr/hal-01742506

K. Nogueira, O. A. Penatti, and J. A. Santos, Towards better exploiting convolutional neural networks for remote sensing scene classification, Pattern Recognition, vol.61, pp.539-556, 2017.
DOI : 10.1016/j.patcog.2016.07.001

URL : http://arxiv.org/pdf/1602.01517

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.1010, issue.1, pp.211-252, 2015.
DOI : 10.1007/978-3-642-15555-0_11

URL : http://dspace.mit.edu/bitstream/1721.1/104944/1/11263_2015_Article_816.pdf

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint, 2014.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the Inception Architecture for Computer Vision, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2818-2826, 2016.
DOI : 10.1109/CVPR.2016.308

URL : http://arxiv.org/pdf/1512.00567

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, How transferable are features in deep neural networks? In Advances in neural information processing systems, pp.3320-3328, 2014.