| 书目名称 | Classification Functions for Machine Learning and Data Mining | | 编辑 | Tsutomu Sasao | | 视频video | | | 概述 | Demonstrates a method to implement machine learning and data mining using look-up tables, rather than neural networks.Enables application on edge computing devices, where low power dissipation and hig | | 丛书名称 | Synthesis Lectures on Digital Circuits & Systems | | 图书封面 |  | | 描述 | This book introduces a novel perspective on machine learning, offering distinct advantages over neural network-based techniques. This approach boasts a reduced hardware requirement, lower power consumption, and enhanced interpretability. The applications of this approach encompass high-speed classifications, including packet classification, network intrusion detection, and exotic particle detection in high-energy physics. Moreover, it finds utility in medical diagnosis scenarios characterized by small training sets and imbalanced data. The resulting rule generated by this method can be implemented either in software or hardware. In the case of hardware implementation, circuit design can employ look-up tables (memory), rather than threshold gates..The methodology described in this book involves extracting a set of rules from a training set, composed of categorical variable vectors and their corresponding classes. Unnecessary variables are eliminated, and the rules are simplified before being transformed into a sum-of-products (SOP) form. The resulting SOP exhibits the ability to generalize and predict outputs for new inputs. The effectiveness of this approach is demonstrated through | | 出版日期 | Book 2024 | | 关键词 | data mining; supervised machine learning; Low-power machine learning; multi-valued logic; memory-based d | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-35347-5 | | isbn_softcover | 978-3-031-35349-9 | | isbn_ebook | 978-3-031-35347-5Series ISSN 1932-3166 Series E-ISSN 1932-3174 | | issn_series | 1932-3166 | | 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 16:47:34
