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» Margin-based first-order rule learning
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AAAI
1994
13 years 6 months ago
Small is Beautiful: A Brute-Force Approach to Learning First-Order Formulas
We describe a method for learning formulas in firstorder logic using a brute-force, smallest-first search. The method is exceedingly simple. It generates all irreducible well-form...
Steven Minton, Ian Underwood
IJCAI
2007
13 years 6 months ago
A Fully Connectionist Model Generator for Covered First-Order Logic Programs
We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples,...
Sebastian Bader, Pascal Hitzler, Steffen Höll...
ICMLA
2010
13 years 2 months ago
Incremental Learning of Relational Action Rules
Abstract--In the Relational Reinforcement learning framework, we propose an algorithm that learns an action model allowing to predict the resulting state of each action in any give...
Christophe Rodrigues, Pierre Gérard, C&eacu...
MLDM
2001
Springer
13 years 9 months ago
Concepts Learning with Fuzzy Clustering and Relevance Feedback
Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications . . . . . . . . . . . . . . . . . 23 R. Schmidt and L. Gierl Are Case-Based Reasoning and...
Bir Bhanu, Anlei Dong
NIPS
2008
13 years 6 months ago
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Jun Zhu, Eric P. Xing, Bo Zhang