| 书目名称 | Interpretability of Computational Intelligence-Based Regression Models |
| 编辑 | Tamás Kenesei,János Abonyi |
| 视频video | |
| 概述 | Authors provide related Matlab code for download.Valuable for researchers, graduate students and practitioners in computational intelligence and machine learning.Real-world examples drawn from process |
| 丛书名称 | SpringerBriefs in Computer Science |
| 图书封面 |  |
| 描述 | .The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression..The authors demonstrate real-world use of the algorithms with examples taken from process engineering, and they support the text with downloadable Matlab code. The book is suitable for researchers, graduate students and practitioners in the areas of computational intelligence and machine learning.. |
| 出版日期 | Book 2015 |
| 关键词 | Fuzzy Logic; Fuzzy c-Regression Clustering; Hinging Hyperplanes; Model Interpretability; Model Predictiv |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-21942-4 |
| isbn_softcover | 978-3-319-21941-7 |
| isbn_ebook | 978-3-319-21942-4Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s) 2015 |
发表于 2025-3-21 16:59:09
