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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Sample exclusion dimension ： ウィキペディア英語版 Sample exclusion dimension In computational learning theory, sample exclusion dimensions arise in the study of exact concept learning with queries. In algorithmic learning theory, a concept over a domain ''X'' is a Boolean function over ''X''. Here we only consider finite domains. A partial approximation ''S'' of a concept ''c'' is a Boolean function over $Y\backslash subseteq\; X$ such that ''c'' is an extension to ''S''. Let ''C'' be a class of concepts and ''c'' be a concept (not necessarily in ''C''). Then a specifying set for c w.r.t. ''C'', denoted by ''S'' is a partial approximation ''S'' of ''c'' such that ''C'' contains at most one extension to ''S''. If we have observed a specifying set for some concept w.r.t. ''C'', then we have enough information to verify a concept in ''C'' with at most one more mind change. The exclusion dimension, denoted by ''XD''(''C''), of a concept class is the maximum of the size of the minimum specifying set of ''c''' with respect to ''C'', where ''c''' is a concept not in ''C''. == References == 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Sample exclusion dimension」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.