What is the translation of " 在监督学习 " in English?

• in supervised learning

Examples of using 在监督学习 in Chinese and their translations into English

在监督学习中，我们使用之前标记过的数据。
In supervised learning, we use data that has been previously labeled.

对于每个训练示例，在监督学习中有一个目标标签；.
For each training example, there is a target label in supervised learning;

在监督学习中，训练集是已标注的。
In supervised learning, the training set is labeled.

此外，我们可以在监督学习方法中使用它。
Also, we can use it in a supervised learning method.

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，就是让计算机为你计算这种关系。
In supervised learning, you are letting the computer work out that relationship for you.

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.

注意，这种方差在监督学习中也是存在的，但是它很少会这么糟糕。
Look, there's variance in supervised learning too, but it's rarely this bad.

在监督学习中，计算机算法的学习分为两个阶段:学习阶段和测试阶段。
In supervised learning, the computer algorithm learns in two stages: the learning phase and the test phase.

例如，在监督学习中，我们总是期望训练使用的输入是与正确的标注相关联。
For example, in supervised learning we always expect that the training input comes associated with the correct label.

在监督学习中，我们假设特征和目标之间是有实际联系的，并且要用一个模型来预测这种未知关系。
In supervised learning, we assume there's a real relationship between feature(s) and target and estimate this unknown relationship with a model.

在监督学习中，计算机算法分两个阶段学习：学习阶段和测试阶段。
In supervised learning, the computer algorithm learns in two stages: the learning phase and the test phase.

在监督学习中，在标记数据的帮助下完成了明确的训练阶段。
In Supervised Learning, there is a well-defined training phase done with the help of labeled data.

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.

对于每个训练示例，在监督学习中有一个目标标签;
In supervised learning, there is a target label for each training example;

更进一步来说，在监督学习中我们有一个数据集，这个数据集被称训练集。
In supervised learning, we have a dataset, which is called the training set.

在监督学习中，系统试图从之前给出的示例中学习。
In supervised learning, the system tries to learn from the previous examples that are given.

在监督学习中，目标是根据一系列输入度量来预测输出度量的值。
In supervised learning, the goal is to predict the value of an outcome measure based on a number of input measures;

在监督学习中，系统试图从前面给出的例子中学习。
In supervised learning, the system tries to learn from the previous examples that are given.

在监督学习中，系统试图从之前给出的例子中学习。
In supervised learning, the system tries to learn from the previous examples that are given.

在监督学习中，人类仍在做着所有繁重的工作，而那些下棋电脑则一无所知。
Humans still do all the heavy lifting in supervised learning and those chess-playing computers know nothing else.

在监督学习和非监督学习中，不管我们当前在模型空间的什么位置，我们可以很容易地获取到学习的信号。
Contrast this with supervised learning and unsupervised learning, we can obtain learning signals cheaply, no matter where we are in the model search space.

在监督学习问题中，很多效果度量标准用来评估预测误差。
For supervised learning problems, many performance metrics measure the number of prediction errors.

标签噪声(Noisylabels)：在监督学习中我们使用标签来训练模型。
Noisy labels: with supervised learning we use labels to train the models.

在这项工作中，我们主要关注的是弥合CNN在监督学习和无监督学习方面差距。
In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning..

简而言之，它指出没有任何一种算法对所有问题都有效，在监督学习(即预测建模)中尤其如此。
In a nutshell, it states that no one algorithm works best for every problem, and it's especially relevant for supervised learning(i.e. predictive modeling).

在这项工作中，我们主要关注的是弥合CNN在监督学习和无监督学习方面差距。
In this work, the main focus is to help bridge the gap between the success of CNN's for supervised learning and unsupervised learning..

在监督学习中更是如此。
This is particularly so in supervised learning.

这点在监督学习尤为如此。
This is particularly so in supervised learning.

这点在监督学习尤为如此。
This is especially so in monitoring studies.