000			03355nam a22005295i 4500
001			978-3-642-33042-1
003			DE-He213
005			20200421111839.0
007			cr nn 008mamaa
008			120913s2013 gw | s |||| 0|eng d
020			_a9783642330421 _9978-3-642-33042-1
024	7		_a10.1007/978-3-642-33042-1 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
245	1	0	_aSynergies of Soft Computing and Statistics for Intelligent Data Analysis _h[electronic resource] / _cedited by Rudolf Kruse, Michael R. Berthold, Christian Moewes, Mar�ia �Angeles Gil, Przemys�aw Grzegorzewski, Olgierd Hryniewicz.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aXVI, 584 p. 74 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Intelligent Systems and Computing, _x2194-5357 ; _v190
505	0		_aPART I Invited Papers -- PART II Foundations -- PART III Statistical Methods -- PART IV Mathematical Aspects -- PART V Engineering.
520			_aIn recent years there has been a growing interest to extend classical methods for data analysis. The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance. Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled. About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS). This book gathers contributions presented at the SMPS'2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis. It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics. Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aComputational intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aKruse, Rudolf. _eeditor.
700	1		_aBerthold, Michael R. _eeditor.
700	1		_aMoewes, Christian. _eeditor.
700	1		_aGil, Mar�ia �Angeles. _eeditor.
700	1		_aGrzegorzewski, Przemys�aw. _eeditor.
700	1		_aHryniewicz, Olgierd. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642330414
830		0	_aAdvances in Intelligent Systems and Computing, _x2194-5357 ; _v190
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-33042-1
912			_aZDB-2-ENG
942			_cEBK
999			_c55472 _d55472