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Post‐supervised based learning of feature weight values

Moustakis Vasilis

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URI: http://purl.tuc.gr/dl/dias/59966E8F-EF16-483A-B262-8C6D8FEBB40B
Year 2006
Type of Item Conference Full Paper
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Bibliographic Citation Moustakis, V., "Post‐supervised based learning of feature weight values," in 5th Hellenic Conference of AI Proceedings. Advances in Artificial Intelligence: Proceedings of the 4th Hellenic Conference in AI, Springer, 2006, pp. 279 – 289. doi: 10.1007/11752912_29 https://doi.org/10.1007/11752912_29
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Summary

The article presents in detail a model for the assessment of feature weight values in context of inductive machine learning. Weight assessment is done based on learned knowledge and can not be used to assess feature values prior to learning. The model is based on Ackoff’s theory of behavioral communication. The model is also used to assess rule value importance. We present model heuristics and present a simple application based on the “play” vs. “not play” golf application. Implications about decision making modeling are discussed.

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