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04218nam a22006135i 4500 |
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978-3-540-78469-2 |
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DE-He213 |
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20151204184003.0 |
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100301s2008 gw | s |||| 0|eng d |
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|a 9783540784692
|9 978-3-540-78469-2
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|a 10.1007/978-3-540-78469-2
|2 doi
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|d GrThAP
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|a Q334-342
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|a TJ210.2-211.495
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|a UYQ
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|a COM004000
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|a 006.3
|2 23
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|a Inductive Logic Programming
|h [electronic resource] :
|b 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers /
|c edited by Hendrik Blockeel, Jan Ramon, Jude Shavlik, Prasad Tadepalli.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2008.
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|a XI, 307 p.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 4894
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|a Invited 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 the Forest Through the Trees -- Building Relational World Models for Reinforcement Learning -- An Inductive Learning System for XML Documents.
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|a Computer science.
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|a Software engineering.
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|a Computer programming.
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|a Algorithms.
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|a Mathematical logic.
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|a Data mining.
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|a Artificial intelligence.
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|a Computer Science.
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|a Artificial Intelligence (incl. Robotics).
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|a Software Engineering/Programming and Operating Systems.
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|a Programming Techniques.
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|a Mathematical Logic and Formal Languages.
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|a Algorithm Analysis and Problem Complexity.
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|a Data Mining and Knowledge Discovery.
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|a Blockeel, Hendrik.
|e editor.
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|a Ramon, Jan.
|e editor.
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|a Shavlik, Jude.
|e editor.
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|a Tadepalli, Prasad.
|e editor.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783540784685
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 4894
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|u http://dx.doi.org/10.1007/978-3-540-78469-2
|z Full Text via HEAL-Link
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|a ZDB-2-SCS
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|a ZDB-2-LNC
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|a Computer Science (Springer-11645)
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