BoostEMM : Transparent boosting using exceptional model mining

Autor: van der Zon, S.B., Zeev Ben Mordehay, O., Vrijdag, T.S., van Ipenburg, W., Veldsink, J., Duivesteijn, W., Pechenizkiy, M., Bordino, I., Caldarelli, G., Fumarola, F., Gullo, F., Squartini, T.

Publikováno v: Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017), 18 September 2017, Skopje, Macedonia, 5-16
STARTPAGE=5;ENDPAGE=16;TITLE=Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017), 18 September 2017, Skopje, Macedonia

Boosting is an iterative ensemble-learning paradigm. Every iteration, a weak predictor learns a classification task, taking into account performance achieved in previous iterations. This is done by assigning weights to individual records of the datas

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::be52bf3a4cb42b18d9a9de9abfb1c462
https://research.tue.nl/nl/publications/ad232d69-ed50-4be9-98c2-fac7a6561560

In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification.

Autor: Boujou, Moncef¹ (AUTHOR) moncef.boujou@univ-amu.fr, Iguernaissi, Rabah¹ (AUTHOR) severine.dubuisson@univ-amu.fr, Nicod, Lionel² (AUTHOR) lionel.nicod@univ-amu.fr, Merad, Djamal¹ (AUTHOR) moncef.boujou@univ-amu.fr, Dubuisson, Séverine¹ (AUTHOR)

Publikováno v: Algorithms. Aug2024, Vol. 17 Issue 8, p352. 26p.

A graph encoder–decoder network for unsupervised anomaly detection.

Autor: Mesgaran, Mahsa¹ (AUTHOR), Hamza, A. Ben¹ (AUTHOR) hamza@ciise.concordia.ca

Publikováno v: Neural Computing & Applications. Nov2023, Vol. 35 Issue 32, p23521-23535. 15p.

Survey of Application of Graph Neural Network in Anomaly Detection. (English)

Autor: CHEN Jiale, CHEN Xu, JING Yongjun, WANG Shuyang

Publikováno v: Journal of Computer Engineering & Applications; 7/1/2024, Vol. 60 Issue 13, p51-65, 15p

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