The goal of learning systems is to generalize. Generalization is commonly based on the set of critical features the system has available. Training set learners typically extract critical features from a random set of examples. While this approach is attractive, it suffers from the exponential growth of the number of features to be searched. The authors propose to extend it by endowing the system with some a priori knowledge, in the form of precepts. Advantages of the augmented system are speed-up, improved generalization, and greater parsimony. The authors present a precept-driven learning algorithm. Its main features include: 1) distributed implementation, 2) bounded learning and execution times; and 3) ability to handle both correct and incorrect precepts. Results of simulations on real-world data demonstrate promise.
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