Mathematical Problems in Data Science Theoretical and Practical Methods /

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learn...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Chen, Li M. (Autor), Su, Zhixun (Autor), Jiang, Bo (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Temas:
Computer networks .
Computer science-Mathematics.
Computer Communication Networks.
Mathematics of Computing.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Introduction: Data Science and BigData Computing
  • Overview of Basic Methods for Data Science
  • Relationship and Connectivity of Incomplete Data Collection
  • Machine Learning for Data Science: Mathematical or Computational
  • Images, Videos, and BigData
  • Topological Data Analysis
  • Monte Carlo Methods and their Applications in Big Data Analysis
  • Feature Extraction via Vector Bundle Learning
  • Curve Interpolation and Financial Curve Construction
  • Advanced Methods in Variational Learning: Segmentation with Intensity Inhomogeneity
  • An On-line Strategy of Groups Evacuation From a Convex Region in the Plane
  • A New Computational Model of Bigdata.

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