Daniel T. Halstead

Learn More
It is notoriously difficult to simultaneously deal with both probabilistic and structural representations in A.I., particularly because probability necessitates a uniform representation of the training examples. In this paper, we show how to build fully-specified probabilistic models from arbitrary propositional case descriptions about terrorist activities.(More)
A key issue in artificial intelligence lies in finding the amount of input detail needed to do successful learning. Too much detail causes overhead and makes learning prone to over-fitting. Too little detail and it may not be possible to learn anything at all. The issue is particularly relevant when the inputs are relational case descriptions, and a very(More)
  • 1