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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 Category utility ： ウィキペディア英語版 Category utility Category utility is a measure of "category goodness" defined in and . It attempts to maximize both the probability that two objects in the same category have attribute values in common, and the probability that objects from different categories have different attribute values. It was intended to supersede more limited measures of category goodness such as "cue validity" (; ) and "collocation index" . It provides a normative information-theoretic measure of the ''predictive advantage'' gained by the observer who possesses knowledge of the given category structure (i.e., the class labels of instances) over the observer who does ''not'' possess knowledge of the category structure. In this sense the motivation for the ''category utility'' measure is similar to the information gain metric used in decision tree learning. In certain presentations, it is also formally equivalent to the mutual information, as discussed below. A review of ''category utility'' in its probabilistic incarnation, with applications to machine learning, is provided in . ==Probability-theoretic definition of Category Utility== The probability-theoretic definition of ''category utility'' given in and is as follows: :$$ CU(C,F) = \tfrac \sum_ p(c_j) \left (marginal probability that feature $f\_i\backslash$ takes on value $k\backslash$, and the term $p(f\_|c\_j)\backslash$ designates the category-conditional probability that feature $f\_i\backslash$ takes on value $k\backslash$ ''given'' that the object in question belongs to category $c\_j\backslash$. The motivation and development of this expression for ''category utility'', and the role of the multiplicand $\backslash textstyle\; \backslash tfrac$ as a crude overfitting control, is given in the above sources. Loosely , the term $\backslash textstyle\; p(c\_j)\; \backslash sum\_\; \backslash sum\_^m\; p(f\_|c\_j)^2$ is the expected number of attribute values that can be correctly guessed by an observer using a probability-matching strategy together with knowledge of the category labels, while $\backslash textstyle\; p(c\_j)\; \backslash sum\_\; \backslash sum\_^m\; p(f\_)^2$ is the expected number of attribute values that can be correctly guessed by an observer the same strategy but without any knowledge of the category labels. Their difference therefore reflects the relative advantage accruing to the observer by having knowledge of the category structure. 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「Category utility」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.