000			03788nam a2200529 i 4500
001			6267269
003			IEEE
005			20220712204615.0
006			m o d
007			cr |n|||||||||
008			151223s2007 maua ob 001 eng d
020			_z9780262072885 _qprint
020			_a9780262256230 _qebook
020			_z0262256231 _qelectronic
035			_a(CaBNVSL)mat06267269
035			_a(IDAMS)0b000064818b423d
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.9.D3 _bI68 2007eb
245	0	0	_aIntroduction to statistical relational learning / _cedited by Lise Getoor, Ben Taskar.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc2007.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2007]
300			_a1 PDF (ix, 586 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aAdaptive computation and machine learning series
500			_a"Index" : an online index is available on the book webpage at http://www.cs.umd.edu/srl-book/index.htm.
504			_aIncludes bibliographical references.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aHandling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
550			_aMade available online by EBSCO.
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aComputer algorithms. _94534
650		0	_aMachine learning _xStatistical methods. _921845
650		0	_aRelational databases. _99971
655		0	_aElectronic books. _93294
700	1		_aGetoor, Lise. _921846
700	1		_aTaskar, Ben. _921847
710	2		_aIEEE Xplore (Online Service), _edistributor. _921848
710	2		_aMIT Press, _epublisher. _921849
776	0	8	_iPrint version _z9780262072885
830		0	_aAdaptive computation and machine learning. _921570
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267269
942			_cEBK
999			_c72927 _d72927