Mathematical Problems in Data Science

Theoretical and Practical Methods

Häftad, Engelska, 2019

Av Li M. Chen, Zhixun Su, Bo Jiang

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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 learning methods.

Produktinformation

Utgivningsdatum2019-03-14
Mått155 x 235 x 13 mm
Vikt359 g
FormatHäftad
SpråkEngelska
Antal sidor213
FörlagSpringer International Publishing AG
ISBN9783319797397

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Hårdvara inom Data och it

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.

“Data science includes mathematical and statistical tools required to find relations and principles behind heterogeneous and possibly unstructured data. It is an emerging field, under active research, and the authors here have attempted to explain existing methods whole introducing some open problems. … Overall, the book offers a collection of papers that describe current trends and future directions along with appropriate references. The presented applications cover a broad spectrum of domains where big data poses challenges.” (Paparao Kayalipati, Computing Reviews, computingreviews.com, September, 2016)