Mathematical Problems in Data Science Theoretical and Practical Methods /

• Main Authors: Chen, Li M. (Author), Su, Zhixun (Author), Jiang, Bo (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2015.
• Edition: 1st ed. 2015.
• Subjects: Computer science.; Computer communication systems.; Computer science > Mathematics.; Computers.; Computer Science.; Information Systems and Communication Service.; Computer Communication Networks.; Mathematics of Computing.

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...

Table of Contents:

• 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.