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pro vyhledávání: '"Nakano, Felipe Kenji"'
The availability of genomic data has grown exponentially in the last decade, mainly due to the development of new sequencing technologies. Based on the interactions between genes (and gene products) extracted from the increasing genomic data, numerou
Externí odkaz:
http://arxiv.org/abs/2207.06237
Tree-ensemble algorithms, such as random forest, are effective machine learning methods popular for their flexibility, high performance, and robustness to overfitting. However, since multiple learners are combined, they are not as interpretable as a
Externí odkaz:
http://arxiv.org/abs/2203.15511
Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to model the behavior of users and to generate recommendations. Typically, t
Externí odkaz:
http://arxiv.org/abs/2203.05954
Autor:
Nakano, Felipe Kenji, Dulfer, Karolijn, Vanhorebeek, Ilse, Wouters, Pieter J., Verbruggen, Sascha C., Joosten, Koen F., Güiza Grandas, Fabian, Vens, Celine, Van den Berghe, Greet
Publikováno v:
In Computer Methods and Programs in Biomedicine June 2024 250
Recently, deep neural networks have expanded the state-of-art in various scientific fields and provided solutions to long standing problems across multiple application domains. Nevertheless, they also suffer from weaknesses since their optimal perfor
Externí odkaz:
http://arxiv.org/abs/2011.02829
Publikováno v:
In Computers in Biology and Medicine January 2023 152
Publikováno v:
In Pattern Recognition January 2022 121
Akademický článek
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Akademický článek
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Autor:
Martiello Mastelini, Saulo, Nakano, Felipe Kenji, Vens, Celine, de Leon Ferreira de Carvalho, Andre Carlos Ponce
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems; October 2023, Vol. 34 Issue: 10 p6755-6767, 13p