Respondents were required to review, revise and verify any flagged data in order to submit their surveys.
如果没有标记的数据,它就无法识别自己的改进,因此它就不知道如何坚持每一个改进。
Without labeled data, it couldn't recognize its own improvements so it wouldn't know to stick with each improvement along the way.
无监督学习策略一般用在没有标记的数据中以发现隐藏结构(例如隐藏的马尔科夫链)。
Unsupervised learning strategies are typically used to discoverhidden structures(such as hidden Markov chains) in unlabeled data.
预期输出称为标签,而输入数据就是“打了标记的数据”。
The expected output is called a label,and the data is‘labeled data'.
再者,非监督式学习试图在无标记的数据中找出隐藏的结构,这主要应用于集群、各种统计分布。
Furthermore, unsupervised learning tries to find a hidden structure in unlabeled data, and it's mostly used for clustering and various statistical distributions.
这是一个很重要的优点,因为未标记的数据比有标记的数据更多。
This is an important benefit becauseunlabeled data are more abundant than labeled data.
此外,若可以使用英语标记的数据,则这些方法可实现几乎免费的零样本转移。
Furthermore, if labelled data in English is available, these methods enable essentially free zero-shot transfer.
分类:样本属于两个或更多类,我们想从已经标记的数据中学习如何预测未标记数据的类别。
Classification: Samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data..
在这种情况下,我们可以使用人工标记的数据,因为相对较少的一部分查询占了很大一部分流量。
(You can feasibly use humanlabelled data in this case because a relatively small fraction of the queries account for a large fraction of the traffic.).
但魔法背后往往意味着单调乏味的过程--精心标记的数据是许多深度学习项目的关键。
But behind that magic there's often drudgery- painstakingly labeled data is key to many deep learning projects.
在监督学习中,用户训练程序以基于已知和标记的数据集生成答案。
In supervised learning,the user trains the program to generate an answer based on a known and labeled data set.
标记的数据集是教师,它将训练您理解数据中的模式。
The label data set is a teacher that will train you to understand the patterns in the data..
在监督学习中,机器在有标记数据的帮助下进行训练,即,即带有正确答案标记的数据。
In Supervised learning,the machine is trained with the help of labelled data ie, data which is tagged with the right answers.
在监督学习中,机器在有标记数据的帮助下进行训练,即,即带有正确答案标记的数据。
In Supervised learning, the machine is trained with the help of labeled data,i.e., data that is tagged with the right answers.
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