Ripple-Down Rules

Regular price €167.40
A01=Byeong Ho Kang
A01=Paul Compton
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AI systems
Author_Byeong Ho Kang
Author_Paul Compton
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Chemical Pathology
commercial deployment
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Counter-intuitive RDR algorithms
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False Branches
Final Conclusion Rules
general ripple-down rules
Ground Glass Opacity
Habitat Conclusion
Hendra Virus
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industrial applications
Inference Cycles
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Knowledge Acquisition
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Machine Learning
Machine learning algorithms
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philosophical issues
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RDR-based system
Refinement Rule
Repeat Inference
research-demonstrated applications
Resultant Knowledge Base
Ripple-down rules
Rule Builder
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SCRDR evaluation
Semi-supervised Learning
softlaunch
Supervised Machine Learning
Unbalanced Data
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Product details

  • ISBN 9780367647667
  • Weight: 520g
  • Dimensions: 156 x 234mm
  • Publication Date: 31 May 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
  • Language: English
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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.

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."