训练集 - 翻译成英语

• the training set
• the training data

在 中文 中使用 训练集 的示例及其翻译为 英语

参赛者的软件根据其对未被纳入训练集的其他图像进行分类的能力进行评判。
Contestants' software was judged on its ability to classify other images that had not been included in the training set.

通过使用训练集，网络的权重和偏差可为线性分类而更新。
Using a training set, the network's weights and bias could be updated for linear classification.

随着训练集规模的增长，优化算法处理大量数据的效率成为瓶颈。
As the size of the training sets increased, the efficiency of optimization algorithms to handle large amounts of data became a bottleneck.

随机森林模型也成功地应用于稀疏训练集和无样本预测，这表明了其在促进合成方法采用方面的价值。
The random forest model was also successfully applied to sparse training sets and out-of-sample prediction, suggesting its value in facilitating adoption of synthetic methodology.

训练集，我们可以对它进行任何大量的探索来建立我们的分类器.
A training set on which we can do any amount of exploration to build our classifier;

每个训练集都是由除特定组别以外的所有样本构成的。
Each training set is thus constituted by all the samples except the ones related to a specific group.

在训练集当中，我们将2217条示例导入x与y;
In case of the training set we will put 2,217 examples into x and y;

如果我们把两个误差得分随着训练集的改变画出来，最终我们会得到两个曲线。
If we plot the evolution of the two error scores as training sets change, we end up with two curves.

接下来，我将70%的数据分成训练集，30%的数据分成测试集。
Next I'm going split 70% of my data into a training set, and 30% into a test set..

生成模型旨在学习训练集的真实数据分布以便生成具有变化的新数据点。
The main aim is to learn the true data distribution of the training set so that the new data points are generated with some variations.

但是当去除掉训练集部分时，它的大小与ObjectNet相当，差不多有5万张照片。
But when its training set is excluded, it's comparable in size to ObjectNet, at 50,000 photos.

训练集-用于训练网络的图像，其结果用于更新模型的权重。
Training set- the images used to train the network, the results of which are used to update the model's weights.

当训练集中的所有输入都处理完了，一个epoch也就完成了。
When all inputs from a training set are processed, one epoch is done.

因此，标签powerset给训练集中的每一个可能的标签组合提供了一个独特的类。
So, label powerset has given a unique class to every possible label combination that is present in the training set.

假如你有一个训练集，其中包含1000个不同的人组成的10000张照片，.
If you have a training set of say 10,000 pictures with 1,000 different persons.

因此，每个训练集合除了与特定组相关的样本之外的所有样本构成。
Each training set is thus constituted by all the samples except the ones related to a specific group.

由于训练集是有限的，未来是不确定的，因此学习理论通常不能保证算法的性能。
Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms.

因此，由训练集所定义的经验分布会替代真实分布。
Thus the empirical distribution defined by the training set replaces the true distribution.

总之，对于一个训练集，总是可以归纳出至少两个（形式）近似的，但完全相反的等式。
In short, given a training set, there are always at least two equally plausible, but totally opposite, generalizations that could be made.

换句话说，如果训练集的规模翻倍，那么训练时间至少要增加4倍。
In other words, if you double your training set size, your training will take at least 4 times longer.