Langue: anglais
Edité par Berlin, Springer Netherlands., 2005
ISBN 10 : 9048100380 ISBN 13 : 9789048100385
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Ajouter au panierXI, 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.
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Langue: anglais
Edité par Springer International Publishing AG, 2023
ISBN 10 : 3031334396 ISBN 13 : 9783031334399
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Ajouter au panierHRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
Langue: anglais
Edité par Springer International Publishing AG, 2023
ISBN 10 : 3031334396 ISBN 13 : 9783031334399
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Langue: anglais
Edité par Springer International Publishing AG, CH, 2023
ISBN 10 : 3031334396 ISBN 13 : 9783031334399
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Ajouter au panierHardback. Etat : 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.
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Ajouter au panierPaperback / softback. Etat : New. New copy - Usually dispatched within 4 working days.
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Langue: anglais
Edité par Springer International Publishing AG, CH, 2023
ISBN 10 : 3031334396 ISBN 13 : 9783031334399
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Ajouter au panierHardback. Etat : 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.
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Ajouter au panierPaperback. Etat : New.
Langue: anglais
Edité par Springer International Publishing AG, Cham, 2023
ISBN 10 : 3031334396 ISBN 13 : 9783031334399
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Ajouter au panierHardcover. Etat : 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.