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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 Domain adaptation ： ウィキペディア英語版 Domain adaptation Domain Adaptation is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning from a source data distribution a well performing model on a different (but related) target data distribution. For instance, one of the tasks of the common spam filtering problem consists in adapting a model from one user (the source distribution) to a new one who receives significantly different emails (the target distribution). Note that, when more than one source distribution is available we talked about multi-source domain adaptation. == Formalization == Let $X$ be the input space (or description space) and let $Y$ be the output space (or label space). The objective of a machine learning algorithm is to learn a mathematical model (a hypothesis) $h:X\backslash to\; Y$ able to affect a label of $Y$ to an example from $X$. This model is learned from a learning sample $S=\backslash \_^m\; \backslash in\; (X\backslash times\; Y)^m$. Usually in supervised learning (without domain adaptation), we suppose that the examples $(x\_i,y\_i)\backslash in\; S$ are drawn i.i.d. from a distribution $D\_S$ of support $X\backslash times\; Y$ (unknown and fixed). The objective is then to learn $h$ (from $S$) such that it commits the least error as possible for labelling new examples coming from the distribution $D\_S$. The main difference between supervised learning and domain adaptation is that in the latter situation we study two different (but related) distributions $D\_S$ and $D\_T$ on $X\backslash times\; Y$. The domain adaptation task then consists of the transfer of knowledge from the source domain $D\_S$ to the target one $D\_T$. The goal is then to learn $h$ (from labeled or unlabelled samples coming from the two domains) such that it commits as little error as possible on the target domain $D\_T$. The major issue is the following: if a model is learned from a source domain, what is its capacity to correctly label data coming from the target domain? == The different types of domain adaptation == There are several contexts of domain adaptation. They differ in the informations considered for the target task. # The unsupervised domain adaptation: the learning sample contains a set of labeled source examples, a set of unlabeled source examples and an unlabeled set of target examples. # The semi-supervised domain adaptation: in this situation, we also consider a "small" set of labeled target examples. # The supervised domain adaptation: all the examples considered are supposed to be labeled. 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「Domain adaptation」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.