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Viser: Mathematical Principles of Topological and Geometric Data Analysis

Mathematical Principles of Topological and Geometric Data Analysis
Søgbar e-bog

Mathematical Principles of Topological and Geometric Data Analysis Vital Source e-bog

Parvaneh Joharinad og Jürgen Jost
(2023)
Springer Nature
699,00 kr.
Leveres umiddelbart efter køb
Mathematical Principles of Topological and Geometric Data Analysis

Mathematical Principles of Topological and Geometric Data Analysis Vital Source e-bog

Parvaneh Joharinad og Jürgen Jost
(2023)
Springer Nature
350,00 kr.
Leveres umiddelbart efter køb
Mathematical Principles of Topological and Geometric Data Analysis

Mathematical Principles of Topological and Geometric Data Analysis Vital Source e-bog

Parvaneh Joharinad og Jürgen Jost
(2023)
Springer Nature
455,00 kr.
Leveres umiddelbart efter køb
Mathematical Principles of Topological and Geometric Data Analysis

Mathematical Principles of Topological and Geometric Data Analysis Vital Source e-bog

Parvaneh Joharinad og Jürgen Jost
(2023)
Springer Nature
675,00 kr.
Leveres umiddelbart efter køb
Mathematical Principles of Topological and Geometric Data Analysis

Mathematical Principles of Topological and Geometric Data Analysis

Parvaneh Joharinad og Jürgen Jost
(2023)
Sprog: Engelsk
Springer International Publishing AG
797,00 kr.
Print on demand. Leveringstid vil være ca 2-3 uger.

Detaljer om varen

  • Vital Source searchable e-book (Reflowable pages)
  • Udgiver: Springer Nature (Juli 2023)
  • Forfattere: Parvaneh Joharinad og Jürgen Jost
  • ISBN: 9783031334405
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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Detaljer om varen

  • Vital Source 90 day rentals (dynamic pages)
  • Udgiver: Springer Nature (Juli 2023)
  • Forfattere: Parvaneh Joharinad og Jürgen Jost
  • ISBN: 9783031334405R90
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.
Licens varighed:
Bookshelf online: 90 dage fra købsdato.
Bookshelf appen: 90 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • Vital Source 180 day rentals (dynamic pages)
  • Udgiver: Springer Nature (Juli 2023)
  • Forfattere: Parvaneh Joharinad og Jürgen Jost
  • ISBN: 9783031334405R180
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.
Licens varighed:
Bookshelf online: 180 dage fra købsdato.
Bookshelf appen: 180 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • Vital Source 365 day rentals (dynamic pages)
  • Udgiver: Springer Nature (Juli 2023)
  • Forfattere: Parvaneh Joharinad og Jürgen Jost
  • ISBN: 9783031334405R365
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.
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: 5 år fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • Hardback
  • Udgiver: Springer International Publishing AG (Juli 2023)
  • Forfattere: Parvaneh Joharinad og Jürgen Jost
  • ISBN: 9783031334399
Introduction.- Topological foundations, hypercomplexes and homology.- Weighted complexes, cohomology and Laplace operators.- The Laplace operator and the geometry of graphs.- Metric spaces and manifolds.- Linear methods: Kernels, variations, and averaging.- Nonlinear schemes: Clustering, feature extraction and dimension reduction.- Manifold learning, the scheme of Laplacian eigenmaps.- Metrics and curvature.
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