Overview
- 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 high speed are essential
- Describes a method that derives classifiers simple enough for human interpretation
Part of the book series: Synthesis Lectures on Digital Circuits & Systems (SLDCS)
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About this book
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 numerous examples and experimental results using the University of California-Irvine (UCI) dataset.
This book is primarily intended for graduate students and researchers in the fields of logic synthesis, machine learning, and data mining. It assumes a foundational understanding of logic synthesis, while familiarity with linear algebra and statistics would be beneficial for readers.
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 numerous examples and experimental results using the University of California-Irvine (UCI) dataset.
This book is primarily intended for graduate students and researchers in the fields of logic synthesis, machine learning, and data mining. It assumes a foundational understanding of logic synthesis, while familiarity with linear algebra and statistics would be beneficial for readers.
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Keywords
Table of contents (12 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the author
Tsutomu Sasao received B.E., M.E., and Ph.D. degrees in Electronics Engineering from Osaka University, Osaka Japan, in 1972, 1974, and 1977, respectively. He has held faculty/research positions at Osaka University, Japan; IBM T. J. Watson Research Center, Yorktown Height, NY; the Naval Postgraduate School, Monterey, CA; Kyushu Institute of Technology, Japan; and Meiji University, Kawasaki, Japan. Currently, he is a visiting researcher of Meiji University, Japan. He is a Life Fellow of the IEEE, and has published many books on logic design.
Bibliographic Information
Book Title: Classification Functions for Machine Learning and Data Mining
Authors: Tsutomu Sasao
Series Title: Synthesis Lectures on Digital Circuits & Systems
DOI: https://doi.org/10.1007/978-3-031-35347-5
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-35346-8Published: 15 July 2023
Softcover ISBN: 978-3-031-35349-9Due: 15 August 2023
eBook ISBN: 978-3-031-35347-5Published: 14 July 2023
Series ISSN: 1932-3166
Series E-ISSN: 1932-3174
Edition Number: 1
Number of Pages: XIII, 144
Number of Illustrations: 19 b/w illustrations, 26 illustrations in colour
Topics: Circuits and Systems, Data Mining and Knowledge Discovery, Machine Learning