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Post‐supervised based learning of feature weight values

Moustakis Vasilis

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URIhttp://purl.tuc.gr/dl/dias/59966E8F-EF16-483A-B262-8C6D8FEBB40B-
Identifierhttps://doi.org/10.1007/11752912_29-
Languageen-
TitlePost‐supervised based learning of feature weight valuesen
CreatorMoustakis Vasilisen
CreatorΜουστακης Βασιληςel
PublisherSpringer Verlagen
Content SummaryThe 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.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-04-
Date of Publication2006-
Bibliographic CitationMoustakis, 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_29el

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