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ML
2008
ACM
150views Machine Learning» more  ML 2008»
13 years 10 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...
GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
14 years 4 months ago
Preventing overfitting in GP with canary functions
Overfitting is a fundamental problem of most machine learning techniques, including genetic programming (GP). Canary functions have been introduced in the literature as a concept ...
Nate Foreman, Matthew P. Evett
ML
2006
ACM
13 years 10 months ago
Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves
Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall i...
Mark Goadrich, Louis Oliphant, Jude W. Shavlik
ECML
2006
Springer
14 years 2 months ago
Skill Acquisition Via Transfer Learning and Advice Taking
We describe a reinforcement learning system that transfers skills from a previously learned source task to a related target task. The system uses inductive logic programming to ana...
Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richa...
AIEDAM
1998
87views more  AIEDAM 1998»
13 years 10 months ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh