TY  - BOOK
AU  - Blockeel,Hendrik
AU  - Ramon,Jan
AU  - Shavlik,Jude
AU  - Tadepalli,Prasad
ED  - SpringerLink (Online service)
TI  - Inductive Logic Programming: 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers 
T2  - Lecture Notes in Computer Science,
SN  - 9783540784692
AV  - Q334-342 
U1  - 006.3 23
PY  - 2008///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Computer software
KW  - Data mining
KW  - Artificial intelligence
KW  - Computer Science
KW  - Artificial Intelligence (incl. Robotics)
KW  - Programming Techniques
KW  - Mathematical Logic and Formal Languages
KW  - Algorithm Analysis and Problem Complexity
KW  - Data Mining and Knowledge Discovery
N1  - 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
UR  - http://dx.doi.org/10.1007/978-3-540-78469-2
ER  -