Ripple-Down Rules | The Alternative to Machine Learning

Paul Compton (u. a.)

ISBN 10: 0367644320 / ISBN 13: 9780367644321

Published by Chapman and Hall/CRC, 2021

About this Item

Ripple-Down Rules | The Alternative to Machine Learning | Paul Compton (u. a.) | Taschenbuch | Englisch | 2021 | Chapman and Hall/CRC | EAN 9780367644321 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 128400351

Bibliographic Details

• Title: Ripple-Down Rules | The Alternative to Machine Learning
• Publisher: Chapman and Hall/CRC
• Publication Date: 2021
• Language: English
• ISBN 10: 0367644320
• ISBN 13: 9780367644321
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 156H x 11D millimeters 234 millimeters width by 156 millimeters height by 11 millimeters depth
• Weight: 310 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Machine learning algorithms hold extraordinary promise, but the reality is that their success depends entirely on the suitability of the data available. This book is about Ripple-Down Rules (RDR), an alternative manual technique for rapidly building AI systems. With a human in the loop, RDR is much better able to deal with the limitations of data.

Ripple-Down Rules: The Alternative to Machine Learning starts by reviewing the problems with data quality and the problems with conventional approaches to incorporating expert human knowledge into AI systems. It suggests that problems with knowledge acquisition arise because of mistaken philosophical assumptions about knowledge. It argues people never really explain how they reach a conclusion, rather they justify their conclusion by differentiating between cases in a context. RDR is based on this more situated understanding of knowledge. The central features of a RDR approach are explained, and detailed worked examples are presented for different types of RDR, based on freely available software developed for this book. The examples ensure developers have a clear idea of the simple yet counter-intuitive RDR algorithms to easily build their own RDR systems.

It has been proven in industrial applications that it takes only a minute or two per rule to build RDR systems with perhaps thousands of rules. The industrial uses of RDR have ranged from medical diagnosis through data cleansing to chatbots in cars. RDR can be used on its own or to improve the performance of machine learning or other methods.

About the Author

Paul Compton initially studied philosophy before majoring in physics. He spent 20 years as a biophysicist at the Garvan Institute of Medical Research, and then 20 years in Computer Science and Engineering at the University of New South Wales, where he was head of school for 12 years and is now an emeritus professor.

Byeong Ho Kang majored in mathematics in Korea, followed by a PhD on Ripple-Down Rules at the University of New South Wales and the algorithm he developed is the basis of most industry RDR applications. He is a professor, with a research focus on applications, and head of the ICT discipline at the University of Tasmania."