- Jamie Callan. 1993. Knowledge-based feature generation for inductive learning. Abstract: Inductive learning is an approach to machine learning in which concepts are learned from examples and counterexamples. One requirement for inductive learning is an explicit representation of the characteristics, or features, that determine whether an object is an example or counterexample. Obvious or easily available representations do not reliably satisfy this requirement, so constructive induction algorithms have been developed to satisfy it automatically. However, there are some features, known to be useful, that have been beyond the capabilities of most constructive induction algorithms. 
