Lingua: Inglese
Editore: Berlin, Springer Netherlands., 2005
ISBN 10: 9048100380 ISBN 13: 9789048100385
Da: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germania
EUR 18,00
Quantità: 1 disponibili
Aggiungi al carrelloXI, 475 p. Softcover. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Condizione: As New. Unread book in perfect condition.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 62,14
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 54,61
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Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New.
Lingua: Inglese
Editore: Springer International Publishing AG, 2023
ISBN 10: 3031334396 ISBN 13: 9783031334399
Da: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: Springer International Publishing AG, 2023
ISBN 10: 3031334396 ISBN 13: 9783031334399
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 63,76
Quantità: 4 disponibili
Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Condizione: New.
Da: Majestic Books, Hounslow, Regno Unito
EUR 62,50
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Aggiungi al carrelloCondizione: New.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 58,98
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Da: Chiron Media, Wallingford, Regno Unito
EUR 56,65
Quantità: 10 disponibili
Aggiungi al carrelloPaperback. Condizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 58,97
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Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2023
ISBN 10: 3031334396 ISBN 13: 9783031334399
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 78,48
Quantità: 2 disponibili
Aggiungi al carrelloHardback. Condizione: New. 2023 ed. This book explores and demonstrates how geometric tools can be used in data analysis. Beginning with a systematic exposition of the mathematical prerequisites, covering topics ranging from category theory to algebraic topology, Riemannian geometry, operator theory and network analysis, it goes on to describe and analyze some of the most important machine learning techniques for dimension reduction, including the different types of manifold learning and kernel methods. It also develops a new notion of curvature of generalized metric spaces, based on the notion of hyperconvexity, which can be used for the topological representation of geometric information.In recent years there has been a fascinating development: concepts and methods originally created in the context of research in pure mathematics, and in particular in geometry, have become powerful tools in machine learning for the analysis of data. The underlying reason for this is that data are typically equipped with somekind of notion of distance, quantifying the differences between data points. Of course, to be successfully applied, the geometric tools usually need to be redefined, generalized, or extended appropriately.Primarily aimed at mathematicians seeking an overview of the geometric concepts and methods that are useful for data analysis, the book will also be of interest to researchers in machine learning and data analysis who want to see a systematic mathematical foundation of the methods that they use.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 61,12
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 63,67
Quantità: 1 disponibili
Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Condizione: New.
Condizione: New. pp. 122.
Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 63,66
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 63,75
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2023
ISBN 10: 3031334396 ISBN 13: 9783031334399
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
Hardback. Condizione: New. 2023 ed. This book explores and demonstrates how geometric tools can be used in data analysis. Beginning with a systematic exposition of the mathematical prerequisites, covering topics ranging from category theory to algebraic topology, Riemannian geometry, operator theory and network analysis, it goes on to describe and analyze some of the most important machine learning techniques for dimension reduction, including the different types of manifold learning and kernel methods. It also develops a new notion of curvature of generalized metric spaces, based on the notion of hyperconvexity, which can be used for the topological representation of geometric information.In recent years there has been a fascinating development: concepts and methods originally created in the context of research in pure mathematics, and in particular in geometry, have become powerful tools in machine learning for the analysis of data. The underlying reason for this is that data are typically equipped with somekind of notion of distance, quantifying the differences between data points. Of course, to be successfully applied, the geometric tools usually need to be redefined, generalized, or extended appropriately.Primarily aimed at mathematicians seeking an overview of the geometric concepts and methods that are useful for data analysis, the book will also be of interest to researchers in machine learning and data analysis who want to see a systematic mathematical foundation of the methods that they use.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 69,90
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. In.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 66,90
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 70,37
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Da: Majestic Books, Hounslow, Regno Unito
EUR 77,41
Quantità: 1 disponibili
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 73,36
Quantità: 3 disponibili
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Da: Chiron Media, Wallingford, Regno Unito
EUR 66,96
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Aggiungi al carrelloPaperback. Condizione: New.
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2023
ISBN 10: 3031334396 ISBN 13: 9783031334399
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condizione: new. Hardcover. This book explores and demonstrates how geometric tools can be used in data analysis. Beginning with a systematic exposition of the mathematical prerequisites, covering topics ranging from category theory to algebraic topology, Riemannian geometry, operator theory and network analysis, it goes on to describe and analyze some of the most important machine learning techniques for dimension reduction, including the different types of manifold learning and kernel methods. It also develops a new notion of curvature of generalized metric spaces, based on the notion of hyperconvexity, which can be used for the topological representation of geometric information.In recent years there has been a fascinating development: concepts and methods originally created in the context of research in pure mathematics, and in particular in geometry, have become powerful tools in machine learning for the analysis of data. The underlying reason for this is that data are typically equipped with somekind of notion of distance, quantifying the differences between data points. Of course, to be successfully applied, the geometric tools usually need to be redefined, generalized, or extended appropriately.Primarily aimed at mathematicians seeking an overview of the geometric concepts and methods that are useful for data analysis, the book will also be of interest to researchers in machine learning and data analysis who want to see a systematic mathematical foundation of the methods that they use. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.