Inductive Logic Programming 11th International Conference, ILP 2001, Strasbourg, France, September 9-11, 2001. Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Rouveirol, Celine (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Sebag, Michele (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.
• Έκδοση: 1st ed. 2001.
• Σειρά: Lecture Notes in Artificial Intelligence ; 2157
• Θέματα: Architecture, Computer.; Software engineering.; Artificial intelligence.; Computer programming.; Mathematical logic.; Algorithms.; Computer System Implementation.; Software Engineering/Programming and Operating Systems.; Artificial Intelligence.; Programming Techniques.; Mathematical Logic and Formal Languages.; Algorithm Analysis and Problem Complexity.

Πίνακας περιεχομένων:

• A Refinement Operator for Theories
• Learning Logic Programs with Neural Networks
• A Genetic Algorithm for Propositionalization
• Classifying Uncovered Examples by Rule Stretching
• Relational Learning Using Constrained Confidence-Rated Boosting
• Induction, Abduction, and Consequence-Finding
• From Shell Logs to Shell Scripts
• An Automated ILP Server in the Field of Bioinformatics
• Adaptive Bayesian Logic Programs
• Towards Combining Inductive Logic Programming with Bayesian Networks
• Demand-Driven Construction of Structural Features in ILP
• Transformation-Based Learning Using Multirelational Aggregation
• Discovering Associations between Spatial Objects: An ILP Application
• ?-Subsumption in a Constraint Satisfaction Perspective
• Learning to Parse from a Treebank: Combining TBL and ILP
• Induction of Stable Models
• Application of Pruning Techniques for Propositional Learning to Progol
• Application of ILP to Cardiac Arrhythmia Characterization for Chronicle Recognition
• Efficient Cross-Validation in ILP
• Modelling Semi-structured Documents with Hedges for Deduction and Induction
• Learning Functions from Imperfect Positive Data.