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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.

Keywords

Noisy Data Radar Image Inductive Logic Programming Logic Programming Program Good Hypothesis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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