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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Blockeel, Hendrik (Επιμελητής έκδοσης), Ramon, Jan (Επιμελητής έκδοσης), Shavlik, Jude (Επιμελητής έκδοσης), Tadepalli, Prasad (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Σειρά: Lecture Notes in Computer Science, 4894
• Θέματα: Computer science.; Software engineering.; Computer programming.; Algorithms.; Mathematical logic.; Data mining.; Artificial intelligence.; Computer Science.; Artificial Intelligence (incl. Robotics).; Software Engineering/Programming and Operating Systems.; Programming Techniques.; Mathematical Logic and Formal Languages.; Algorithm Analysis and Problem Complexity.; Data Mining and Knowledge Discovery.

Πίνακας περιεχομένων:

• 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.