000			03492nam a22004575i 4500
001			978-3-642-29461-7
003			DE-He213
005			20140220083316.0
007			cr nn 008mamaa
008			120425s2012 gw | s |||| 0|eng d
020			_a9783642294617 _9978-3-642-29461-7
024	7		_a10.1007/978-3-642-29461-7 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aDenoeux, Thierry. _eeditor.
245	1	0	_aBelief Functions: Theory and Applications _h[electronic resource] : _bProceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012 / _cedited by Thierry Denoeux, Marie-Hélène Masson.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2012.
300			_aXII, 444p. 96 illus., 54 illus. in color. _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 and Soft Computing, _x1867-5662 ; _v164
505	0		_aFrom the content: On belief functions and random sets -- Evidential Multi-label classification method using the Random k-Label sets approach -- An Evidential Improvement for Gender Profiling -- An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal Semantics -- An evidential pattern matching approach for vehicle identification -- Comparison between a Bayesian approach and a method based on continuous belief functions for pattern recognition -- Prognostic by classification of predictions combining similarity-based estimation and belief functions -- Adaptative initialisation of a EvKNN classification algorithm.
520			_aThe theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories.   This volume contains the proceedings of the 2nd International Conference on Belief Functions that was held in Compiègne, France on 9-11 May 2012. It gathers 51 contributions describing recent developments both on theoretical issues (including approximation methods, combination rules, continuous belief functions, graphical models and independence concepts) and applications in various areas including classification, image processing, statistics and intelligent vehicles.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aMasson, Marie-Hélène. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642294600
830		0	_aAdvances in Intelligent and Soft Computing, _x1867-5662 ; _v164
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-29461-7
912			_aZDB-2-ENG
999			_c103038 _d103038