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008			100301s2008 gw | s |||| 0|eng d
020			_a9783540784692 _9978-3-540-78469-2
024	7		_a10.1007/978-3-540-78469-2 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
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072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
245	1	0	_aInductive Logic Programming _h[electronic resource] : _b17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers / _cedited by Hendrik Blockeel, Jan Ramon, Jude Shavlik, Prasad Tadepalli.
250			_a1st ed. 2008.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2008.
300			_aXI, 307 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v4894
505	0		_aInvited Talks -- Learning with Kernels and Logical Representations -- Beyond Prediction: Directions for Probabilistic and Relational Learning -- Extended Abstracts -- Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract) -- Learning Directed Probabilistic Logical Models Using Ordering-Search -- Learning to Assign Degrees of Belief in Relational Domains -- Bias/Variance Analysis for Relational Domains -- Full Papers -- Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases -- Clustering Relational Data Based on Randomized Propositionalization -- Structural Statistical Software Testing with Active Learning in a Graph -- Learning Declarative Bias -- ILP :- Just Trie It -- Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning -- Empirical Comparison of “Hard” and “Soft” Label Propagation for Relational Classification -- A Phase Transition-Based Perspective on Multiple Instance Kernels -- Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates -- Applying Inductive Logic Programming to Process Mining -- A Refinement Operator Based Learning Algorithm for the Description Logic -- Foundations of Refinement Operators for Description Logics -- A Relational Hierarchical Model for Decision-Theoretic Assistance -- Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming -- Revising First-Order Logic Theories from Examples Through Stochastic Local Search -- Using ILP to Construct Features for Information Extraction from Semi-structured Text -- Mode-Directed Inverse Entailment for Full Clausal Theories -- Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns -- Relational Macros for Transfer in Reinforcement Learning -- Seeing theForest Through the Trees -- Building Relational World Models for Reinforcement Learning -- An Inductive Learning System for XML Documents.
650		0	_aArtificial intelligence.
650		0	_aSoftware engineering.
650		0	_aComputer programming.
650		0	_aMachine theory.
650		0	_aAlgorithms.
650		0	_aData mining.
650	1	4	_aArtificial Intelligence.
650	2	4	_aSoftware Engineering.
650	2	4	_aProgramming Techniques.
650	2	4	_aFormal Languages and Automata Theory.
650	2	4	_aAlgorithms.
650	2	4	_aData Mining and Knowledge Discovery.
700	1		_aBlockeel, Hendrik. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aRamon, Jan. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aShavlik, Jude. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aTadepalli, Prasad. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783540784685
776	0	8	_iPrinted edition: _z9783540847694
830		0	_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v4894
856	4	0	_uhttps://doi.org/10.1007/978-3-540-78469-2
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