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Titlebook: Explainable AI and Other Applications of Fuzzy Techniques; Proceedings of the 2 Julia Rayz,Victor Raskin,Vladik Kreinovich Conference proce

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發(fā)表于 2025-3-21 17:11:05 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Explainable AI and Other Applications of Fuzzy Techniques
副標(biāo)題Proceedings of the 2
編輯Julia Rayz,Victor Raskin,Vladik Kreinovich
視頻videohttp://file.papertrans.cn/320/319280/319280.mp4
概述Is of interest to practitioners, researchers, graduate students, and anyone interested in problem-solving fuzziness.Presents many artificial intelligence (AI) techniques that do not explain their reco
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: Explainable AI and Other Applications of Fuzzy Techniques; Proceedings of the 2 Julia Rayz,Victor Raskin,Vladik Kreinovich Conference proce
描述.This book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques..This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI—in short, to anyone who is interested in problems involving fuzziness and AI in general..
出版日期Conference proceedings 2022
關(guān)鍵詞Fuzzy Systems; Soft Computing; Fuzzy Information Processing; Computational Intelligence; AI; NAFIPS; NAFIP
版次1
doihttps://doi.org/10.1007/978-3-030-82099-2
isbn_softcover978-3-030-82098-5
isbn_ebook978-3-030-82099-2Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Against One Method: Contemplation in Contextequivalence relation between the above mentioned systems under relaxed conditions. As the above proofs are constructive, this method allows us to transform between the considered Neural Networks and Takagi-Sugeno systems. The interpretability of the proposed system is discussed and future research d
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Wim J. van der Linden,Ronald K. Hambletonthe computation of the relative variance of an interval Type-2 fuzzy set using the KM algorithm needs more computations than using the original KM algorithm in order to obtain their boundaries since the variance combined with the KM algorithm results in a nonlinear behavior. A modified KM algorithm
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