000			03495nam a22005775i 4500
001			978-3-319-13644-8
003			DE-He213
005			20200420221252.0
007			cr nn 008mamaa
008			150303s2014 gw | s |||| 0|eng d
020			_a9783319136448 _9978-3-319-13644-8
024	7		_a10.1007/978-3-319-13644-8 _2doi
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
072		7	_aUYQ _2bicssc
072		7	_aTJFM1 _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aNatarajan, Sriraam. _eauthor.
245	1	0	_aBoosted Statistical Relational Learners _h[electronic resource] : _bFrom Benchmarks to Data-Driven Medicine / _cby Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014.
300			_aVIII, 74 p. 25 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computer Science, _x2191-5768
505	0		_aIntroduction -- Statistical Relational Learning -- Boosting (Bi-)Directed Relational Models -- Boosting Undirected Relational Models -- Boosting in the presence of missing data -- Boosting Statistical Relational Learning in Action -- Appendix: Booster System.
520			_aThis SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.
650		0	_aComputer science.
650		0	_aHealth informatics.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650		0	_aStatistics.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aHealth Informatics.
700	1		_aKersting, Kristian. _eauthor.
700	1		_aKhot, Tushar. _eauthor.
700	1		_aShavlik, Jude. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319136431
830		0	_aSpringerBriefs in Computer Science, _x2191-5768
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-13644-8
912			_aZDB-2-SCS
942			_cEBK
999			_c52672 _d52672