000			02924nam a22003618i 4500
001			CR9781139035613
003			UkCbUP
005			20200124160247.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			110301s2012||||enk o ||1 0|eng|d
020			_a9781139035613 (ebook)
020			_z9780521190176 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA276.8 _b.S84 2012
082	0	0	_a006.3/1 _223
100	1		_aSugiyama, Masashi, _d1974- _eauthor.
245	1	0	_aDensity ratio estimation in machine learning / _cMasashi Sugiyama, Taiji Suzuki, Takafumi Kanamori.
264		1	_aCambridge : _bCambridge University Press, _c2012.
300			_a1 online resource (xii, 329 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_aDensity estimation -- Moment matching -- Probabilistic classification -- Density fitting -- Density-ratio fitting -- Unified framework -- Direct density-ratio estimation with dimensionality reduction -- Importance sampling -- Distribution comparison -- Mutual information estimation -- Conditional probability estimation -- Parametric convergence analysis -- Non-parametric convergence analysis -- Parametric two-sample test -- Non-parametric numerical stability analysis -- Conclusions and future directions.
520			_aMachine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.
650		0	_aEstimation theory.
650		0	_aMachine learning.
700	1		_aSuzuki, Taiji, _d1981- _eauthor.
700	1		_aKanamori, Takafumi, _d1971- _eauthor.
776	0	8	_iPrint version: _z9780521190176
856	4	0	_uhttps://doi.org/10.1017/CBO9781139035613
999			_c519104 _d519102