Open Access
Issue
MATEC Web Conf.
Volume 308, 2020
2019 8th International Conference on Transportation and Traffic Engineering (ICTTE 2019)
Article Number 05002
Number of page(s) 4
Section Intelligent Transportation and System Design
DOI https://doi.org/10.1051/matecconf/202030805002
Published online 12 February 2020
  1. Maciejewski Henryk, Mazurkiewicz Jacek, Skowron Krzysztof, Walkowiak Tomasz, Neural Networks for Vehicle Recognition, in Proc. of the 6th International Conference on Microelectronics for Neural Networks, Evolutionary and Fuzzy Systems, 1998, pp.292–296. [Google Scholar]
  2. Zhang Dian Ye, Jian Prof Jin, Zhi-Zheng Assoc Prof Guo. Exploration into Road Traffic Accident Prevention Research System. China Safety Science Journal, Vol.17, No.7, 2007, pp.132-138. [Google Scholar]
  3. Luo Xiang Long, Niu. Vehicle recognition by acoustic signals based on EMD and SVM. Applied Acoustics, Vol.29, No.3, 2010, pp.178-183. [Google Scholar]
  4. Salamon Justin, Bello Juan. Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification. IEEE Signal Processing Letters, Vol. 99, 2016, pp.1-4. [Google Scholar]
  5. Piczak Karol J. Environmental sound classification with convolutional neural networks. IEEE International Workshop on Machine Learning for Signal Processing, 2015, pp.1-4. [Google Scholar]
  6. H Bae S, I Choi, S Kim N, Acoustic scene classification using parallel combination of LSTM and CNN, Proceedings of the Detection and Classification of Acoustic Scenes and Events, 2016, pp.11-15. [Google Scholar]
  7. Xu J, Xiang L, Liu Q, et al. Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images, IEEE Transactions on Medical Imaging, Vol.35, No.1, 2016, pp.119-130. [CrossRef] [Google Scholar]
  8. Chandar A.P.S., Lauly S, Larochelle H, et al. An autoencoder approach to learning bilingual word representations, International Conference on Neural Information Processing Systems. MIT Press, 2014, pp.1853-1861. [Google Scholar]
  9. Goodfellow I.J., Le Q.V., Saxe A.M., et al. Measuring invariances in deep networks, International Conference on Neural Information Processing Systems, 2009, pp.646-654. [Google Scholar]
  10. Mairal J, Bach F, Ponce J, et al. Online Learning for Matrix Factorization and Sparse Coding, Journal of Machine Learning Research, Vol.11, No.1, 2009, pp.19-60. [Google Scholar]
  11. Hinton G.E., Salakhutdinov R.R.. Reducing the Dimensionality of Data with Neural Networks, Science, 313(5786), 2006, pp.504-507. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  12. Phan Huy, Maaß Marco, Mazur Radoslaw, Mertins Alfred. Random Regression Forests for Acoustic Event Detection and Classification. IEEE/ACM Transactions on acoustic Speech & Language Processing, Vol.23, No.1, 2015, pp.20-31. [CrossRef] [Google Scholar]
  13. Garcia-Pedrajas N, Hervas-Martinez C, Munoz-Perez J. COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Transactions on Neural Networks, Vol.14, No.3, 2003, pp.575-596. [CrossRef] [Google Scholar]
  14. Pal, M. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 2005, 26 (1):217-222. [CrossRef] [Google Scholar]
  15. Zhang M.L. , Zhou Z.H.. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 2007, 40 (7):2038-2048. [CrossRef] [Google Scholar]
  16. Wei Y , Zhao Y , Lu C , et al. Cross-Modal Retrieval With CNN Visual Features: A New Baseline. IEEE Transactions on Cybernetics, 2017, 47 (2):449-460. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.