翻訳と辞書 Words near each other ・ Active heave compensation ・ Active hexose correlated compound ・ Active imagination ・ Active immunization ・ Active immunotherapy ・ Active ingredient ・ Active intellect ・ Active Interest Media ・ Active Islamic Youth ・ Active Kidz ・ Active labour market policies ・ Active Lancer ・ Active laser medium ・ Active layer ・ Active learning ・ Active learning (machine learning) ・ Active Learning in Higher Education ・ Active Liberty ・ Active listening ・ Active living ・ Active Living by Design ・ Active load ・ Active management ・ Active matrix ・ Active matter ・ Active measures ・ Active Measures Working Group ・ Active Member ・ Active message ・ Active metabolite 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 Active learning (machine learning) ： ウィキペディア英語版 Active learning (machine learning) Active learning is a special case of semi-supervised machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature it is sometimes also called optimal experimental design.〔.〕 There are situations in which unlabeled data is abundant but manually labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm be overwhelmed by uninformative examples. Recent developments are dedicated to hybrid active learning〔 and active learning in a single-pass (on-line) context,〔 combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning. ==Definitions== Let $T$ be the total set of all data under consideration. For example, in a protein engineering problem, $T$ would include all proteins that are known to have a certain interesting activity and all additional proteins that one might want to test for that activity. During each iteration, $i$, $T$ is broken up into three subsets #$\backslash mathbf\_$: Data points where the label is known. #$\backslash mathbf\_$: Data points where the label is unknown. #$\backslash mathbf\_$: A subset of $T\_$ that is chosen to be labeled. Most of the current research in active learning involves the best method to choose the data points for $T\_$. 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「Active learning (machine learning)」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.