翻訳と辞書 Words near each other ・ Classified advertising ・ Classified information ・ Classified information in Russia ・ Classified information in the United Kingdom ・ Classified information in the United States ・ Classified Information Procedures Act ・ Classified magazine ・ Classified pricing ・ Classified Records ・ Classified United States website ・ Classified Ventures ・ Classified X ・ Classifier ・ Classifier (linguistics) ・ Classifier (UML) ・ Classifier chains ・ Classifier handshape ・ Classifying space ・ Classifying space for O(n) ・ Classifying space for U(n) ・ Classifying topos ・ ClassiKhan ・ ClassiKid ・ Classilla ・ Classique des Alpes ・ Classis ・ Classis Britannica ・ Classis Flavia Moesica ・ Classis Misenensis ・ Classis Ravennas Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Classifier chains ： ウィキペディア英語版 Classifier chains Classifier chains is a machine learning method for problem transformation in multi-label classification. It combines the computational efficiency of the Binary Relevance method while still being able to take the label dependencies into account for classification. == Problem transformation == Problem transformation methods transform a multi-label classification problem in one or more single-label classification problems. In such a way existing single-label classification algorithms such as SVM and Naive Bayes can be used without modification. Several problem transformation methods exist. One of them is Binary Relevance method (BR). Given a set of labels $\backslash mathit\backslash ,$ and a data set with instances of the form $\backslash mathit\backslash ,$ where $\backslash mathit\backslash ,$ is a feature vector and $Y\; \backslash subseteq\; L$ is a set of labels assigned to the instance. BR transforms the data set into $\backslash left\; \backslash vert\; L\; \backslash right\; \backslash vert$ data sets and learns $\backslash left\; \backslash vert\; L\; \backslash right\; \backslash vert$ binary classifiers $H:\; X\; \backslash rightarrow\; \backslash$ for each label $l\; \backslash in\; L$. During this process the information about dependencies between labels is not preserved. This can lead to a situation where a set of labels is assigned to an instance although these labels never co-occur together in the data set. Thus, information about label co-occurrence can help to assign correct label combinations. Loss of this information can in some cases lead to decrease of the classification performance. Other approach, which takes into account label correlations is Label Powerset method (LP). Each different combination of labels in a data set is considered to be a single label. After transformation a single-label classifier $H:\; X\; \backslash rightarrow\; \backslash mathcal(L)$ is trained where $\backslash mathcal(L)$ is the power set of all labels in $\backslash mathit\backslash ,$. The main drawback of this approach is that the number of label combinations grows exponentionally with the number of labels. For example, a multi-label data set with 10 labels can have up to $2^\; =\; 1024$ label combinations. This increases the run-time of classification. Classifier Chains method is based on the BR method and it is efficient even on a big number of labels. Furthermore, it considers dependencies between labels. 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「Classifier chains」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.