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008			120329s2012 flua sb 001 0 eng d
020			_a9781439867648 (ebook : PDF)
040			_aBD-DhSAU _cBD-DhSAU
090			_aQ325.5 _b.S73 2012
092			_a006.31 _bS797
245	0	0	_aStatistical learning and data science _h[electronic resource] / _cedited by Mireille Gettler Summa ... [et al.].
260			_aBoca Raton : _bCRC Press, _cc2012.
300			_axv, 227 p. : _bcol. ill.
490	0		_aChapman & Hall/CRC computer science & data analysis series
504			_aIncludes bibliographical references (p. 205-223) and index.
505	0		_aI. Statistical and machine learning -- II. Data science, foundations, and applications -- III. Complex data.
520			_a"Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data world that we inhabit.Statistical Learning and Data Science is a work of reference in the rapidly evolving context of converging methodologies. It gathers contributions from some of the foundational thinkers in the different fields of data analysis to the major theoretical results in the domain. On the methodological front, the volume includes conformal prediction and frameworks for assessing confidence in outputs, together with attendant risk. It illustrates a wide range of applications, including semantics, credit risk, energy production, genomics, and ecology. The book also addresses issues of origin and evolutions in the unsupervised data analysis arena, and presents some approaches for time series, symbolic data, and functional data.Over the history of multidimensional data analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and unsupervised data analysis, provide great capability for addressing the digital data deluge. Exploring the foundations and recent breakthroughs in the field, Statistical Learning and Data Science demonstrates how data analysis can improve personal and collective health and the well-being of our social, business, and physical environments. "-- _cProvided by publisher.
530			_aAlso available in print edition.
538			_aMode of access: World Wide Web.
650		0	_aMachine learning _xStatistical methods.
650		0	_aData mining.
655		7	_aElectronic books. _2lcsh
700	1		_aSumma, Mireille Gettler.
776	1		_z9781439867631 (hardback : alk. paper)
856	4	0	_uhttp://marc.crcnetbase.com/isbn/9781439867648 _qapplication/PDF
999			_c11802 _d11801