A framework for low complexity static learning
Emil Gizdarski, Hideo Fujiwara · 2002
In this paper, we present a new data structure for a complete implication graph and two techniques for low complexity static learning. We show that using static indirect /spl and/-implications and super gate extraction some hard-to-detect static and dynamic indirect implications are easily derived during static and dynamic learning as well as branch and bound search. Experimental results demonstrated the effectiveness of the proposed data structure and learning techniques.