Learning from Noisy Data Using a Non-covering ILP Algorithm

  • Andrej Oblak
  • Ivan Bratko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6489)

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrej Oblak
    • 1
  • Ivan Bratko
    • 1
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

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