000			03692nam a2200517 i 4500
001			6267199
003			IEEE
005			20220712204556.0
006			m o d
007			cr |n|||||||||
008			151223s2008 maua ob 001 eng d
020			_a9780262255103 _qebook
020			_z9780262170055 _qhardcover : alk. paper
020			_z0262170051 _qhardcover : alk. paper
020			_z0262255103 _qelelelectronic
035			_a(CaBNVSL)mat06267199
035			_a(IDAMS)0b000064818b4161
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ325.5 _b.D37 2009eb
082	0	4	_a006.3/1 _222
245	0	0	_aDataset shift in machine learning / _c[edited by] Joaquin Qui�nonero-Candela ... [et al.].
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc2009.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2008]
300			_a1 PDF (xv, 229 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aNeural information processing series
504			_aIncludes bibliographical references and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aDataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors [cut for catalog if necessary]Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Bruckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Muller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Scholkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
550			_aMade available online by Ebrary.
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aMachine learning. _91831
650		0	_aMachine learning _xMathematical models. _921452
655		0	_aElectronic books. _93294
700	1		_aQui�nonero-Candela, Joaquin. _921453
710	2		_aIEEE Xplore (Online Service), _edistributor. _921454
710	2		_aMIT Press, _epublisher. _921455
776	0	8	_iPrint version _z9780262170055
830		0	_aNeural information processing series _921456
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267199
942			_cEBK
999			_c72857 _d72857