Zobrazeno 1 - 7
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pro vyhledávání: '"Temraz, Mohammed"'
Autor:
Temraz, Mohammed, Keane, Mark T.
Learning from class imbalanced datasets poses challenges for many machine learning algorithms. Many real-world domains are, by definition, class imbalanced by virtue of having a majority class that naturally has many more instances than its minority
Externí odkaz:
http://arxiv.org/abs/2111.03516
Publikováno v:
IJCAI-21 Workshop on DL-CBR-AML, July 2021
Recently, it has been proposed that fruitful synergies may exist between Deep Learning (DL) and Case Based Reasoning (CBR); that there are insights to be gained by applying CBR ideas to problems in DL (what could be called DeepCBR). In this paper, we
Externí odkaz:
http://arxiv.org/abs/2104.14461
Autor:
Temraz, Mohammed, Kenny, Eoin, Ruelle, Elodie, Shalloo, Laurence, Smyth, Barry, Keane, Mark T
Climate change poses a major challenge to humanity, especially in its impact on agriculture, a challenge that a responsible AI should meet. In this paper, we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by supporting grass
Externí odkaz:
http://arxiv.org/abs/2104.04008
Autor:
Temraz, Mohammed, Keane, Mark T.
Publikováno v:
In Machine Learning with Applications 15 September 2022 9
Publikováno v:
Revista Entramado; jul-dic2024, Vol. 20 Issue 2, p1-15, 15p
Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability chall
This book constitutes the proceedings of the 29th International Conference on Case-Based Reasoning, ICCBR 2021, which took place in Salamanca, Spain, during September 13-16, 2021. The 21 papers presented in this volume were carefully reviewed and se