Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Verri, Filipe Alves Neto"'
Autor:
Bertoli, Gustavo de Carvalho, Junior, Lourenço Alves Pereira, Verri, Filipe Alves Neto, Santos, Aldri Luiz dos, Saotome, Osamu
Most research using machine learning (ML) for network intrusion detection systems (NIDS) uses well-established datasets such as KDD-CUP99, NSL-KDD, UNSW-NB15, and CICIDS-2017. In this context, the possibilities of machine learning techniques are expl
Externí odkaz:
http://arxiv.org/abs/2110.13655
Autor:
Verri, Filipe Alves Neto, Gueleri, Roberto Alves, Zheng, Qiusheng, Zhang, Junbao, Zhao, Liang
We present a network community-detection technique based on properties that emerge from a nature-inspired system of aligning particles. Initially, each vertex is assigned a random-direction unit vector. A nonlinear dynamic law is established so that
Externí odkaz:
http://arxiv.org/abs/1802.04186
Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectiv
Externí odkaz:
http://arxiv.org/abs/1710.09300
Autor:
Lopes, Paulo Victor, Silveira, Leonardo, Guimaraes Aquino, Roberto Douglas, Ribeiro, Carlos Henrique, Skoogh, Anders, Verri, Filipe Alves Neto
Publikováno v:
International Journal of Computer Integrated Manufacturing; Oct/Nov2024, Vol. 37 Issue 10/11, p1252-1269, 18p
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 2, pp. 405-418, Feb. 2018. doi: 10.1109/TNNLS.2016.2626341
The emergence of collective dynamics in neural networks is a mechanism of the animal and human brain for information processing. In this paper, we develop a computational technique using distributed processing elements in a complex network, which are
Externí odkaz:
http://arxiv.org/abs/1603.01182
Akademický článek
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Akademický článek
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Autor:
Verri, Filipe Alves Neto, Gueleri, Roberto Alves, Zheng, Qiusheng, Zhang, Junbao, Zhao, Liang
Publikováno v:
European Physical Journal: Special Topics; Oct2021, Vol. 230 Issue 14/15, p2843-2855, 13p
Autor:
Verri, Filipe Alves Neto
Machine learning enables machines to learn automatically from data. In literature, graph-based methods have received increasing attention due to their ability to learn from both local and global information. In these methods, each data instance is re
Publikováno v:
International Journal of Pattern Recognition & Artificial Intelligence. Nov2016, Vol. 30 Issue 9, p-1. 19p.