Learning from Noisy Data Using a Non-covering ILP Algorithm

摘要

In this paper we describe the non-covering inductive logic programming program HYPER/N, concentrating mainly on noise handling as well as some other mechanisms that improve learning. We perform some experiments with HYPER/N on synthetic weather data with artificially added noise, and on real weather data to learn to predict the movement of rain from radar rain images and synoptic data.