000			03299nam a2200481 i 4500
001			6267539
003			IEEE
005			20220712204734.0
006			m o d
007			cr |n|||||||||
008			151223s2012 maua ob 001 eng d
020			_z9780262017091 _qprint
020			_a9780262301220 _qelectronic
020			_z0262301229 _qelectronic
035			_a(CaBNVSL)mat06267539
035			_a(IDAMS)0b000064818b458f
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ325.5 _b.S845 2012eb
100	1		_aSugiyama, Masashi, _d1974- _923350
245	1	0	_aMachine learning in non-stationary environments : _bintroduction to covariate shift adaptation / _cMasashi Sugiyama and Motoaki Kawanabe.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc2012.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2012]
300			_a1 PDF (xiv, 261 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aAs the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aMachine learning. _91831
655		0	_aElectronic books. _93294
700	1		_aKawanabe, Motoaki. _923351
710	2		_aIEEE Xplore (Online Service), _edistributor. _923352
710	2		_aMIT Press, _epublisher. _923353
776	0	8	_iPrint version _z9780262017091
830		0	_aAdaptive computation and machine learning. _921570
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267539
942			_cEBK
999			_c73192 _d73192