000			04122nam a22005055i 4500
001			978-3-319-25127-1
003			DE-He213
005			20200420221248.0
007			cr nn 008mamaa
008			151215s2015 gw | s |||| 0|eng d
020			_a9783319251271 _9978-3-319-25127-1
024	7		_a10.1007/978-3-319-25127-1 _2doi
050		4	_aQA75.5-76.95
072		7	_aUT _2bicssc
072		7	_aCOM069000 _2bisacsh
072		7	_aCOM032000 _2bisacsh
082	0	4	_a005.7 _223
100	1		_aChen, Li M. _eauthor.
245	1	0	_aMathematical Problems in Data Science _h[electronic resource] : _bTheoretical and Practical Methods / _cby Li M. Chen, Zhixun Su, Bo Jiang.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_aXV, 213 p. 64 illus., 42 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction: 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			_aThis 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	_aComputer science.
650		0	_aComputer communication systems.
650		0	_aComputer science _xMathematics.
650		0	_aComputers.
650	1	4	_aComputer Science.
650	2	4	_aInformation Systems and Communication Service.
650	2	4	_aComputer Communication Networks.
650	2	4	_aMathematics of Computing.
700	1		_aSu, Zhixun. _eauthor.
700	1		_aJiang, Bo. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319251257
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-25127-1
912			_aZDB-2-SCS
942			_cEBK
999			_c52439 _d52439