Application of Boolean expression minimization to learning via hierarchical generalization
Jianhua Chen · 1994
Concept learning through hierarchical generalization is an important technique in machine learning. The main result of this paper shows that this particular type of learning can be done using the well-known technique of boolean expression minimization. The boolean formulation unifies the various techniques suggested previously for hierarchical generalizations. It gives better conceptual clarity and a computationally efficient method for this type of learning. In particular, learning from relational databases can also be cast in the framework of boolean minimization.