Inductive Logic Programming - Lecture Notes in Computer Science
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Published By Springer Berlin Heidelberg

9783540784685, 9783540784692
Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases

2008 ◽  
pp. 29-38 ◽  
Author(s):  
Claudia d’Amato ◽  
Nicola Fanizzi ◽  
Floriana Esposito
Keyword(s):  
Knowledge Bases

scholarly journals Learning Directed Probabilistic Logical Models Using Ordering-Search

2008 ◽  
pp. 24-24
Author(s):  
Daan Fierens ◽  
Jan Ramon ◽  
Maurice Bruynooghe ◽  
Hendrik Blockeel
Keyword(s):  
Logical Models ◽  
Probabilistic Logical Models

scholarly journals A Phase Transition-Based Perspective on Multiple Instance Kernels

2008 ◽  
pp. 112-121 ◽  
Author(s):  
Romaric Gaudel ◽  
Michèle Sebag ◽  
Antoine Cornuéjols
Keyword(s):  
Phase Transition

Applying Inductive Logic Programming to Process Mining

2008 ◽  
pp. 132-146 ◽  
Author(s):  
Evelina Lamma ◽  
Paola Mello ◽  
Fabrizio Riguzzi ◽  
Sergio Storari
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Process Mining ◽  
Inductive Logic

Bias/Variance Analysis for Relational Domains

2008 ◽  
pp. 27-28 ◽  
Author(s):  
Jennifer Neville ◽  
David Jensen
Keyword(s):  
Variance Analysis ◽  
Relational Domains ◽  
Bias Variance

Mode-Directed Inverse Entailment for Full Clausal Theories

2008 ◽  
pp. 225-238 ◽  
Author(s):  
Oliver Ray ◽  
Katsumi Inoue
Keyword(s):  
Inverse Entailment

Learning Declarative Bias

2008 ◽  
pp. 63-77 ◽  
Author(s):  
Will Bridewell ◽  
Ljupčo Todorovski
Keyword(s):  
Declarative Bias

scholarly journals Relational Macros for Transfer in Reinforcement Learning

2008 ◽  
pp. 254-268 ◽  
Author(s):  
Lisa Torrey ◽  
Jude Shavlik ◽  
Trevor Walker ◽  
Richard Maclin
Keyword(s):  
Reinforcement Learning

scholarly journals Building Relational World Models for Reinforcement Learning

2008 ◽  
pp. 280-291
Author(s):  
Trevor Walker ◽  
Lisa Torrey ◽  
Jude Shavlik ◽  
Richard Maclin
Keyword(s):  
Reinforcement Learning
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