國立虎尾科技大學 |

Mathematical Problems in Data Science = Theoretical and Practical Methods /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Mathematical Problems in Data Science/ by Li M. Chen, Zhixun Su, Bo Jiang.
Reminder of title:	Theoretical and Practical Methods /
Author:	Chen, Li M.
other author:	Su, Zhixun.
Description:	XV, 213 p. 64 illus., 42 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computers. -
Online resource:	https://doi.org/10.1007/978-3-319-25127-1
ISBN:	9783319251271

Mathematical Problems in Data Science = Theoretical and Practical Methods /
Chen, Li M.

Mathematical Problems in Data ScienceTheoretical and Practical Methods /[electronic resource] :by Li M. Chen, Zhixun Su, Bo Jiang. - 1st ed. 2015. - XV, 213 p. 64 illus., 42 illus. in color.online resource.

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.

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. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.

ISBN: 9783319251271

Standard No.: 10.1007/978-3-319-25127-1doiSubjects--Topical Terms:

565115
Computers.

LC Class. No.: QA75.5-76.95

Dewey Class. No.: 005.7

Mathematical Problems in Data Science = Theoretical and Practical Methods /
LDR:03792nam a22003975i 4500 001 961925
003 DE-He213
005 20200701151030.0
007 cr nn 008mamaa
008 201211s2015 gw | s |||| 0|eng d
020 $a 9783319251271 $9 978-3-319-25127-1
024 7 $a 10.1007/978-3-319-25127-1 $2 doi
035 $a 978-3-319-25127-1
050 4 $a QA75.5-76.95
072 7 $a UT $2 bicssc
072 7 $a COM069000 $2 bisacsh
072 7 $a UT $2 thema
082 0 4 $a 005.7 $2 23
100 1 $a Chen, Li M. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1070308
245 1 0 $a Mathematical Problems in Data Science $h [electronic resource] : $b Theoretical and Practical Methods / $c by Li M. Chen, Zhixun Su, Bo Jiang.
250 $a 1st ed. 2015.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a XV, 213 p. 64 illus., 42 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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.
520 $a 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. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.
650 0 $a Computers. $3 565115
650 0 $a Computer communication systems. $3 1115394
650 0 $a Computer science—Mathematics. $3 1253519
650 1 4 $a Information Systems and Communication Service. $3 669203
650 2 4 $a Computer Communication Networks. $3 669310
650 2 4 $a Mathematics of Computing. $3 669457
700 1 $a Su, Zhixun. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1070309
700 1 $a Jiang, Bo. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1070310
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319251257
776 0 8 $i Printed edition: $z 9783319251264
776 0 8 $i Printed edition: $z 9783319797397
856 4 0 $u https://doi.org/10.1007/978-3-319-25127-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)