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008			151226s2015 gw | s |||| 0|eng d
020			_a9783319237084 _z978-3-319-23708-4
024	7		_a10.1007/978-3-319-23708-4 _2doi
050		4	_aQA8.9-QA10.3
072		7	_aUYA _2bicssc
072		7	_aMAT018000 _2bisacsh
072		7	_aUYA _2thema005.131 _223
245	1	0	_aInductive Logic Programming : _b24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers / _cedited by Jesse Davis, Jan Ramon.
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
490	0		_aLecture Notes in Artificial Intelligence ; _v9046
505	0		_aReframing on Relational Data -- Inductive Learning using Constraint-driven Bias -- Nonmonotonic Learning in Large Biological Networks -- Construction of Complex Aggregates with Random Restart Hill-Climbing -- Logical minimisation of meta-rules within Meta-Interpretive Learning -- Goal and plan recognition via parse trees using prefix and infix probability computation -- Effectively creating weakly labeled training examples via approximate domain knowledge -- Learning Prime Implicant Conditions From Interpretation Transition -- Statistical Relational Learning for Handwriting Recognition -- The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions -- Towards machine learning of predictive models from ecological data -- PageRank, ProPPR, and Stochastic Logic Programs -- Complex aggregates over clusters of elements -- On the Complexity of Frequent Subtree Mining in Very Simple Structures.
520			_aThis book constitutes the thoroughly refereed post-conference proceedings of the 24th International Conference on Inductive Logic Programming, ILP 2014, held in Nancy, France, in September 2014. The 14 revised papers presented were carefully reviewed and selected from 41 submissions. The papers focus on topics such as the inducing of logic programs, learning from data represented with logic, multi-relational machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining.
650		0	_aComputer science.
650		0	_aArtificial intelligence.
650		0	_aLogic design.
650	1	4	_aMathematical Logic and Formal Languages. _0http://scigraph.springernature.com/things/product-market-codes/I16048
650	2	4	_aArtificial Intelligence. _0http://scigraph.springernature.com/things/product-market-codes/I21000
650	2	4	_aProgramming Techniques. _0http://scigraph.springernature.com/things/product-market-codes/I14010
650	2	4	_aInformation Systems Applications (incl. Internet). _0http://scigraph.springernature.com/things/product-market-codes/I18040
650	2	4	_aLogics and Meanings of Programs. _0http://scigraph.springernature.com/things/product-market-codes/I1603X
650	2	4	_aComputation by Abstract Devices. _0http://scigraph.springernature.com/things/product-market-codes/I16013
700	1		_aDavis, Jesse. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aRamon, Jan. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-319-23708-4 _3Springer eBooks _zOnline access link to the resource
912			_aZDB-2-SCS
912			_aZDB-2-LNC
999			_c200433954 _d52166
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041			_aeng