An instance-based approach to pattern association learning with application to the English past tense verb domain

摘要

We present a method for using instance-based learning to acquire Pattern Association (PA) rules and apply it to the English past tense verb domain. To retrieve exemplars, we introduce a distance metric for PA, Pattern Association Metric for Exemplar-based Learning Algorithms (PAMELA), which extends that used in PEBLS for classification. The associated pattern is then built by adapting that of the retrieved exemplar(s). We show that our algorithm IBPA-3, which uses exemplar distance weighting and attribute weighting, improves upon the C4.5-based SPA algorithm and, when tested on the difficult case of irregular verbs, out-performs the current state of the art algorithm for this problem, the relational learner, FOIDL.