BEXA: set covering vs. neural network knowledge acquisition-a comparative review

Machine learning approaches to knowledge acquisition usually employ a symbolic method based on search, heuristically guided through the concept space to avoid the combinatorial explosion of possible concept descriptions to be examined. Neural networks, on the other hand usually employ gradient based minimization of a cost function to acquire classificational knowledge. This paper presents a new symbolic set covering algorithm for rule induction, reviews five learning paradigms and compares that to knowledge acquisition by a neural network classifier