EUR 54,20
Anzahl: 1 verfügbar
In den WarenkorbPAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Anbieter: GreatBookPrices, Columbia, MD, USA
EUR 58,24
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Anbieter: GreatBookPrices, Columbia, MD, USA
EUR 59,92
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: As New. Unread book in perfect condition.
Zustand: New. pages cm First edition Includes bibliographical references and index.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 58,47
Anzahl: 3 verfügbar
In den WarenkorbZustand: New. pages cm.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 51,03
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 57,54
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 55,11
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: Chiron Media, Wallingford, Vereinigtes Königreich
EUR 54,54
Anzahl: 1 verfügbar
In den Warenkorbpaperback. Zustand: New.
Zustand: New. 2024. 1st Edition. paperback. . . . . .
Anbieter: THE SAINT BOOKSTORE, Southport, Vereinigtes Königreich
EUR 55,51
Anzahl: 1 verfügbar
In den WarenkorbOther book format. Zustand: New. New copy - Usually dispatched within 4 working days. 920.
Verlag: Taylor and Francis Ltd, GB, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Sprache: Englisch
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
EUR 82,29
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: 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.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 67,29
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 442 pages. 10.00x7.00x10.00 inches. In Stock.
Zustand: New. 2024. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.
EUR 51,04
Anzahl: 1 verfügbar
In den WarenkorbZustand: NEW.
Verlag: Taylor & Francis Ltd, London, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Sprache: Englisch
Anbieter: CitiRetail, Stevenage, Vereinigtes Königreich
EUR 68,50
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: 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.
Zustand: 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.
Anbieter: GreatBookPrices, Columbia, MD, USA
EUR 123,96
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Anbieter: Lucky's Textbooks, Dallas, TX, USA
EUR 122,77
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: As New. Unread book in perfect condition.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 132,08
Anzahl: 3 verfügbar
In den WarenkorbZustand: New.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 123,04
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: 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.
Zustand: New. 1st edition NO-PA16APR2015-KAP.
Verlag: Taylor and Francis Ltd, GB, 2024
ISBN 10: 036769820X ISBN 13: 9780367698201
Sprache: Englisch
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
EUR 73,21
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: 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.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 136,62
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: THE SAINT BOOKSTORE, Southport, Vereinigtes Königreich
EUR 135,96
Anzahl: 1 verfügbar
In den WarenkorbHardback. Zustand: New. New copy - Usually dispatched within 4 working days. 209.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 135,95
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 187,14
Anzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 448 pages. 10.00x7.00x0.94 inches. In Stock.