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Argument-Based Machine Learning

  • Ivan Bratko
  • Jure Žabkar
  • Martin Možina
Chapter

The most common form of machine learning (ML) is learning from examples, also called inductive learning . Usually the problem of learning from examples is stated as: Given examples, find a theory that is consistent with the examples. We say that such a theory is induced from the examples. Roughly, we say that a theory is consistent with the examples if the examples can be derived from the theory. In the case of learning from imperfect, noisy data, we may not insist on perfect consistency between the examples and the theory. In such cases, a shorter and only “approximately” consistent theory may be more appropriate.

Keywords

Inductive Logic Programing Beam Search Covering Algorithm Positive Argument Negative Argument 
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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Notes

Acknowledgments

This work was carried out under the auspices of the European Commission’s Information Society Technologies (IST) programme, through Project ASPIC (IST-FP6- 002307). It was also supported by the Slovenian research agency ARRS.

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Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

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