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EUR 57,87
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Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 56,12
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 60,46
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 59,97
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Añadir al carritoCondición: New. pages cm First edition Includes bibliographical references and index.
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 65,94
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 58,37
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Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 73,33
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Añadir al carritoPaperback. Condición: 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.
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EUR 56,32
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 59,32
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Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Original o primera edición
EUR 67,43
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Añadir al carritoCondición: New. 2024. 1st Edition. paperback. . . . . .
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EUR 60,16
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Añadir al carritoOther book format. Condición: New. New copy - Usually dispatched within 4 working days.
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Añadir al carritoCondición: New. 2024. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.
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Librería: Revaluation Books, Exeter, Reino Unido
EUR 89,95
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Añadir al carritoPaperback. Condición: Brand New. 442 pages. 10.00x7.00x10.00 inches. In Stock.
Librería: moluna, Greven, Alemania
EUR 63,54
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Añadir al carritoCondición: 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.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 129,77
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Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
EUR 128,61
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 130,38
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Librería: Majestic Books, Hounslow, Reino Unido
EUR 133,26
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Librería: California Books, Miami, FL, Estados Unidos de America
EUR 144,89
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Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Librería: Rarewaves.com UK, London, Reino Unido
EUR 68,02
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: 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.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 130,17
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Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 137,89
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 137,88
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 154,69
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Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 159,54
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Librería: Revaluation Books, Exeter, Reino Unido
EUR 194,65
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Añadir al carritoHardcover. Condición: Brand New. 448 pages. 10.00x7.00x0.94 inches. In Stock.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 57,01
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Añadir al carritoCondición: New. Print on Demand pages cm.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 59,38
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Añadir al carritoCondición: New. PRINT ON DEMAND pages cm.