Argument-Based Machine Learning
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.
KeywordsInductive Logic Programing Beam Search Covering Algorithm Positive Argument Negative Argument
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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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