Sciweavers

5 search results - page 1 / 1
» ExOpaque: A Framework to Explain Opaque Machine Learning Mod...
Sort
View
ICTAI
2007
IEEE
14 years 1 months ago
ExOpaque: A Framework to Explain Opaque Machine Learning Models Using Inductive Logic Programming
In this paper we developed an Inductive Logic Programming (ILP) based framework ExOpaque that is able to extract a set of Horn clauses from an arbitrary opaque machine learning mo...
Yunsong Guo, Bart Selman
ML
2008
ACM
150views Machine Learning» more  ML 2008»
13 years 7 months ago
Learning probabilistic logic models from probabilistic examples
Abstract. We revisit an application developed originally using Inductive Logic Programming (ILP) by replacing the underlying Logic Program (LP) description with Stochastic Logic Pr...
Jianzhong Chen, Stephen Muggleton, José Car...
ICMLA
2010
13 years 4 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...
MLG
2007
Springer
14 years 1 months ago
Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning
Abstract. We revisit an application developed originally using Inductive Logic Programming (ILP) by replacing the underlying Logic Program (LP) description with Stochastic Logic Pr...
Jianzhong Chen, Stephen Muggleton, Jose Santos
JAIR
2008
157views more  JAIR 2008»
13 years 7 months ago
Qualitative System Identification from Imperfect Data
Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to ...
George Macleod Coghill, Ashwin Srinivasan, Ross D....