000			03232nam a22004335i 4500
003			DE-He213
005			20231104114429.0
007			cr nn 008mamaa
008			150507s2015 gw | s |||| 0|eng d
020			_a9783319157269 _z978-3-319-15726-9
024	7		_a10.1007/978-3-319-15726-9 _2doi
050		4	_aQ334-342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema006.3 _223
100	1		_aAmini, Massih-Reza. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aLearning with Partially Labeled and Interdependent Data / _cby Massih-Reza Amini, Nicolas Usunier.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Introduction to learning theory -- Semi-supervised learning -- Learning with interdependent data.
520			_aThis book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus do not meet the demands of many web-related tasks. Later chapters deal with the development of learning methods for ranking entities in a large collection with respect to precise information needed. In some cases, learning a ranking function can be reduced to learning a classification function over the pairs of examples. The book proves that this task can be efficiently tackled in a new framework: learning with interdependent data. Researchers and professionals in machine learning will find these new perspectives and solutions valuable. Learning with Partially Labeled and Interdependent Data is also useful for advanced-level students of computer science, particularly those focused on statistics and learning.
650		0	_aArtificial intelligence.
650		0	_aData mining.
650		0	_aStatistics.
650	1	4	_aArtificial Intelligence. _0http://scigraph.springernature.com/things/product-market-codes/I21000
650	2	4	_aData Mining and Knowledge Discovery. _0http://scigraph.springernature.com/things/product-market-codes/I18030
650	2	4	_aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. _0http://scigraph.springernature.com/things/product-market-codes/S17020
700	1		_aUsunier, Nicolas. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-319-15726-9 _3Springer eBooks _zOnline access link to the resource
912			_aZDB-2-SCS
999			_c200434538 _d52750
942			_2lcc _cEBK
041			_aeng