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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels.
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Añadir al carritoTaschenbuch. Condición: Neu. The Practice of Crowdsourcing | Omar Alonso | Taschenbuch | Synthesis Lectures on Information Concepts, Retrieval, and Services | xix | Englisch | 2019 | Springer | EAN 9783031011900 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels. 152 pp. Englisch.
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not .
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Publicado por Springer, Springer Mai 2019, 2019
ISBN 10: 3031011902 ISBN 13: 9783031011900
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 152 pp. Englisch.