Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 58,71
Convertir monedaCantidad disponible: 4 disponibles
Añadir al carritoCondición: New. pages cm First edition Includes bibliographical references and index.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 62,95
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Idioma: Inglés
Librería: Grand Eagle Retail, Mason, OH, Estados Unidos de America
EUR 65,28
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. 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 Knowledge Guided Machine Learning provides an introduction to this rapidly growing field by discussing some of the common themes of research in SGML, using illustrative examples and case studies from diverse application domains and research communities as contributed book chapters. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 57,60
Convertir monedaCantidad disponible: 3 disponibles
Añadir al carritoCondición: New. pages cm.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 63,29
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 57,96
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. In.
Publicado por Chapman and Hall/CRC -, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Idioma: Inglés
Librería: Chiron Media, Wallingford, Reino Unido
EUR 54,93
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritopaperback. Condición: New.
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 55,91
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoOther book format. Condición: New. New copy - Usually dispatched within 4 working days. 920.
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Original o primera edición
EUR 66,10
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoCondición: New. 2024. 1st Edition. paperback. . . . . .
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 62,99
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 69,81
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 80,50
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoCondición: New. 2024. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Idioma: Inglés
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 63,56
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. 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 Knowledge Guided Machine Learning provides an introduction to this rapidly growing field by discussing some of the common themes of research in SGML, using illustrative examples and case studies from diverse application domains and research communities as contributed book chapters. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 67,77
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: Brand New. 442 pages. 10.00x7.00x10.00 inches. In Stock.
EUR 51,41
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoCondición: NEW.
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
EUR 69,00
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. 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 Knowledge Guided Machine Learning provides an introduction to this rapidly growing field by discussing some of the common themes of research in SGML, using illustrative examples and case studies from diverse application domains and research communities as contributed book chapters. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
EUR 63,54
Convertir monedaCantidad disponible: 1 disponibles
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 122,12
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
EUR 120,94
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 133,27
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 130,12
Convertir monedaCantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 136,95
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoHardback. Condición: New. New copy - Usually dispatched within 4 working days. 209.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 146,23
Convertir monedaCantidad disponible: 3 disponibles
Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 137,61
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. In.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 136,93
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 149,67
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 153,61
Convertir monedaCantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 182,61
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoHardcover. Condición: Brand New. 448 pages. 10.00x7.00x0.94 inches. In Stock.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 61,32
Convertir monedaCantidad disponible: 4 disponibles
Añadir al carritoCondición: New. PRINT ON DEMAND pages cm.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 65,00
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Knowledge Guided Machine Learning provides an introduction to this rapidly growing field by discussing some of the common themes of research in SGML, using illustrative examples and case studies from diverse application domains and research communities as contributed book chapters.