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Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
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Idioma: Inglés
Publicado por Chapman and Hall/CRC 2021-03-25, 2021
ISBN 10: 0367644320 ISBN 13: 9780367644321
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 W.
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Añadir al carritoTaschenbuch. Condición: Neu. 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.
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.