Inductive Logic Programming

17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers

328 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

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.

• Reihe: Lecture Notes in Computer Science
• Themen: Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI) Wissensbasierte Systeme, Expertensysteme
• ISBN: 9783540784685
• Sprache: Englisch
• Erscheinungsdatum: 14.03.2008
• Größe: 235 x 155 mm
• Verlag: Springer
• Herausgegeben von: Jude Shavlik, Jan Ramon, Hendrik Blockeel, Prasad Tadepalli