SØG - mellem flere end 8 millioner bøger:

Søg på: Titel, forfatter, forlag - gerne i kombination.
Eller blot på isbn, hvis du kender dette.

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
725,00 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
362,00 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
471,00 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
725,00 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
775,00 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 some kind 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: ubegrænset 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 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 some kind 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 some kind 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 some kind 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.
De oplyste priser er inkl. moms

Polyteknisk Boghandel

har gennem mere end 50 år været studieboghandlen på DTU og en af Danmarks førende specialister i faglitteratur.

 

Vi lagerfører et bredt udvalg af bøger, ikke bare inden for videnskab og teknik, men også f.eks. ledelse, IT og meget andet.

Læs mere her


Trykt eller digital bog?

Ud over trykte bøger tilbyder vi tre forskellige typer af digitale bøger:

 

Vital Source Bookshelf: En velfungerende ebogsplatform, hvor bogen downloades til din computer og/eller mobile enhed.

 

Du skal bruge den gratis Bookshelf software til at læse læse bøgerne - der er indbygget gode værktøjer til f.eks. søgning, overstregning, notetagning mv. I langt de fleste tilfælde vil du samtidig have en sideløbende 1825 dages online adgang. Læs mere om Vital Source bøger

 

Levering: I forbindelse med købet opretter du et login. Når du har installeret Bookshelf softwaren, logger du blot ind og din bog downloades automatisk.

 

 

Adobe ebog: Dette er Adobe DRM ebøger som downloades til din lokale computer eller mobil enhed.

 

For at læse bøgerne kræves særlig software, som understøtter denne type. Softwaren er gratis, men du bør sikre at du har rettigheder til installere software på den maskine du påtænker at anvende den på. Læs mere om Adobe DRM bøger

 

Levering: Et download link sendes pr email umiddelbart efter købet.

 


Ibog: Dette er en online bog som kan læses på udgiverens website. 

Der kræves ikke særlig software, bogen læses i en almindelig browser.

 

Levering: Vores medarbejder sender dig en adgangsnøgle pr email.

 

Vi gør opmærksom på at der ikke er retur/fortrydelsesret på digitale varer.