| 书目名称 | Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance | | 编辑 | Tom Rutkowski | | 视频video | | | 概述 | Proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable.Provides the main idea of the explainable recommenders outlined with | | 丛书名称 | Studies in Computational Intelligence | | 图书封面 |  | | 描述 | .The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.. | | 出版日期 | Book 2021 | | 关键词 | Computational Intelligence; Explainable Artificial Intelligence; Neuro-Fuzzy Modeling; Finance Applicat | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-75521-8 | | isbn_softcover | 978-3-030-75523-2 | | isbn_ebook | 978-3-030-75521-8Series ISSN 1860-949X Series E-ISSN 1860-9503 | | issn_series | 1860-949X | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 18:02:54
