Inductive Logic Programming 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers /
Inductive Logic Programming 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers / [electronic resource] :
edited by Hendrik Blockeel, Jan Ramon, Jude Shavlik, Prasad Tadepalli.
- 1st ed. 2008.
- XI, 307 p. online resource.
- Lecture Notes in Artificial Intelligence, 4894 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 4894 .
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 theForest Through the Trees -- Building Relational World Models for Reinforcement Learning -- An Inductive Learning System for XML Documents.
9783540784692
10.1007/978-3-540-78469-2 doi
Artificial intelligence.
Software engineering.
Computer programming.
Machine theory.
Algorithms.
Data mining.
Artificial Intelligence.
Software Engineering.
Programming Techniques.
Formal Languages and Automata Theory.
Algorithms.
Data Mining and Knowledge Discovery.
Q334-342 TA347.A78
006.3
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 theForest Through the Trees -- Building Relational World Models for Reinforcement Learning -- An Inductive Learning System for XML Documents.
9783540784692
10.1007/978-3-540-78469-2 doi
Artificial intelligence.
Software engineering.
Computer programming.
Machine theory.
Algorithms.
Data mining.
Artificial Intelligence.
Software Engineering.
Programming Techniques.
Formal Languages and Automata Theory.
Algorithms.
Data Mining and Knowledge Discovery.
Q334-342 TA347.A78
006.3