Trade-Off between Requirement of Learning and Computational Cost

摘要

Machine learning in real-world situations some- times starts from an initial collection of training instances; learn- ing then proceeds off and on as new training instances come intermittently. The idea of two-phase learning has then been pro- posed here for effectively solving the learning problems in which training instances come in this two-stage way. Four two-phase learning algorithms based on the learning method PRISM have also been proposed for inducing rules from training instances. These alternatives form a spectrum, showing achievement of the requirement of PRISM (keeping down the number of irrelevant attributes) heavily dependent on the spent computational cost.