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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Condizione: New.
Condizione: As New. Unread book in perfect condition.
Da: Majestic Books, Hounslow, Regno Unito
EUR 58,24
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Condizione: New. pages cm First edition Includes bibliographical references and index.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 57,33
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 54,34
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Aggiungi al carrellopaperback. Condizione: New.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Prima edizione
EUR 66,16
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Aggiungi al carrelloCondizione: New. 2024. 1st Edition. paperback. . . . . .
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 55,30
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Aggiungi al carrelloOther book format. Condizione: New. New copy - Usually dispatched within 4 working days. 920.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 66,73
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Da: Revaluation Books, Exeter, Regno Unito
EUR 67,04
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Aggiungi al carrelloPaperback. Condizione: Brand New. 442 pages. 10.00x7.00x10.00 inches. In Stock.
Editore: Taylor and Francis Ltd, GB, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Lingua: Inglese
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 82,45
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Aggiungi al carrelloPaperback. Condizione: New. Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML.
Condizione: New. 2024. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.
EUR 50,85
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Aggiungi al carrelloCondizione: NEW.
EUR 63,54
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Aggiungi al carrelloCondizione: New. Anuj Karpatne is an Assistant Professor in the Department of Computer Science at Virginia Tech. His research focuses on pushing on the frontiers of knowledge-guided machine learning by combining scientific knowledge and data in the design and learning of.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 120,24
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 122,28
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Condizione: As New. Unread book in perfect condition.
Da: Majestic Books, Hounslow, Regno Unito
EUR 131,56
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 122,73
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Da: preigu, Osnabrück, Germania
EUR 71,20
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Knowledge Guided Machine Learning | Accelerating Discovery using Scientific Knowledge and Data | Anuj Karpatne (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | Chapman and Hall/CRC | EAN 9780367698201 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Condizione: New. 1st edition NO-PA16APR2015-KAP.
Editore: Taylor and Francis Ltd, GB, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Lingua: Inglese
Da: Rarewaves.com UK, London, Regno Unito
EUR 72,94
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: New. Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 136,11
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Aggiungi al carrelloCondizione: New. In.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 135,45
Quantità: 1 disponibili
Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days. 209.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 135,44
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 151,35
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New.