Abstract
A method is proposed for supervised learning to classify bit strings for three classes. The learner was modeled by two formal con- cepts: transformation system and stability optimization. Even though a small set of short examples were used in the training stage, all bit strings of any length were classi.ed correctly in the online recognition stage. The learner successfully learned to devise a way by means of metric calcula- tions to classify bit strings according to 3-parity-ness while the learner was never told the concept of 3-parity-ness.
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Chan, T.Y.T. (2000). Supervised Classification for the Triple Parity Strings. In: Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2000. Lecture Notes in Computer Science, vol 1904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45331-8_33
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DOI: https://doi.org/10.1007/3-540-45331-8_33
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