In some cases, it's not so much the treatment of the animals on set in the studio that has activists worried; it's the off set training and living conditions that are raising concerns.
在某些情况下，让积极分子担心的并不是摄影棚里对待动物的方式，而是片场外的训练和生活条件。
A corollary of this principle is that a learning algorithm should never be evaluated for its results in the training set because this shows no evidence of an ability to generalize to unseen instances.
这个原理的一个推论是，一种学习算法永远不会对它训练集的结果进行评估，因为对于一种未知的事例而言，没有证据表明算法具有概括它们的能力。
This gives you a much larger training set for each trial, meaning that your algorithm will have enough data to learn from, but it also gives a fairly large number of tests (20 instead of 5 or 10).
对每次尝试来说，训练集都非常大，这意味着你的算法有足够的数据进行学习，而且这样一来也提供了足够多的测试次数(20次，而不是5次或10次)。
Nevertheless, if you're in a bind for data, this can yield passable results with lower variance than simply using one test set and one training set.
尽管如此，比起只是简单的使用一个测试集和一个训练集，这种方法可以产生比较低的差异，还算是一个可以说得过去的结果。
The neural net uses these to modify its weights, and it aims to match its classifications with the targets in the training set.
神经网络用这些来调整权系数，其目的使培训中的目标与其分类相匹配。
If, for instance, you only have 20 samples, there's not much data to use for a training set and still leave a significant test set.
比如，你现在仅仅只有20个样本，对于训练集和有效的测试集来说，没有太多的数据。
The examination also shows I have a lot fewer examples overall in comparison to the history/science run, because each file is much smaller than either the history or science training set.
此外，与 “历史/科学” 结果相比，得到了示例也少了很多，因为每个文件都比历史或科学训练集小很多。
In 1960 a spirited animal lover with no scientific training set up camp in Tanganyika's Gombe Stream Game Reserve to observe chimpanzees.
1960年，一位没有接受过任何科学训练的勇敢动物爱好者在坦噶尼喀湖畔的贡贝野生动物保护区安营扎寨开始观察大猩猩。
The classification problem is then to find a good predictor for the class y of any sample of the same distribution (not necessarily from the training set) given only an observation [6]:338.
所谓的分类问题就是指对于相同分布的样本x（可以是训练集以外的样本），都能预知其所属的类。
These inputs, often called the "training set", are the examples from which the agent tries to learn.
这些输入通常被称作“训练集”（原文为training set，译者注），它们是Agent尝试学习的样本。
In 1960 a spirited animal lover with no scientific training set up camp in Tanganyika’s Gombe Stream Game Reserve to observe chimpanzees.
1960年，一位英姿飒爽的动物爱好者在坦噶尼喀（Tanganyika）的冈贝河野生动物保护区（Gombe StreamGame Reserve）扎下了营地，虽然没有接受过任何科学训练，但她此行却是为了观察黑猩猩而来。
Choose the file bmw-test.arff, which contains 1,500 records that were not in the training set we used to create the model.
选择文件bmw - test . arff，内含1,500条记录，而这些记录在我们用来创建模型的训练集中是没有的。
We've already talked a bit about the fact that algorithms may over-fit the training set.
我们已经提到了一点有关算法可能会与训练集过拟合（over-fit）的细节。
Similarly, with machine learning algorithms, a common problem is over-fitting the data and essentially memorizing the training set rather than learning a more general classification technique.
同样，对于机器学习算法，一个通常的问题是过适合(原文为over -fitting，译者注)数据，以及主要记忆训练集，而不是学习过多的一般分类技术。
The training set of data will be memorized, making the network useless on new data sets.
训练集数据将被记忆，而使在处理新数据方面网络无用。
Ensure that use training set is selected so we use the data set we just loaded to create our model.
请确保选中usetraining set以便我们使用刚载入的这个数据集来创建我们的模型。
A new SVM iterative algorithm is proposed, aiming at the problem that the speeds of learning and classifying are slow in large training set.
针对SVM方法在大样本情况下学习和分类速度慢的问题，提出了大样本情况下的一种新的SVM迭代训练算法。
Machine learning and data mining techniques are applied to acquire knowledge and build a concept reasoning network based on semantic dictionary and large training set.
在已有的英语语义词典及大量训练集的基础上，应用机器学习、数据挖掘等技术进行知识获取并最终形成若干个概念推理网。
The training set is not needed in clustering but the accuracy is lower.
聚类不需要训练集，但准确率较低。
In selecting vectors, the algorithm also USES the class label of training set and the judgment information of class mean vector.
在特征向量的选择中，本算法还用到了训练集的类别标签和类别平均向量的判别信息。
Decision tree algorithm is that the category knowledge of the training set is mined through built high precision and small-scale decision tree.
决策树算法通过构造精度高、小规模的决策树采掘训练集中的分类知识。
We select training sets and test sets in many different software releases and discuss the relation between training set and the prediction accuracy.
通过采集通信软件的不同发布版本的测试历史数据，讨论了训练集数据的选择与预测精度之间的关系。
Classification and clustering are both commonly used data mining methods. The advantage of classification is that the accuracy is higher, but the labeled training set is needed.
分类和聚类都是常用的数据挖掘方法，分类的优点是准确率较高，但需要带有类别标注的训练集；
Use the class information of training set to build the model, and extract the feature benefit to classification.
利用训练文档的类信息对文本分类模型进行建模，提取对分类贡献较大的特征。
And the classifier's performance may be improved by filtering the samples in the training set.
在训练网页分类器时，对网页样本集进行有效地筛选有可能改善分类器的性能。
Moreover, as class density estimates cannot be derived for such a training set, class posterior probabilities cannot be estimated.
更进一步地，因种类分布密度无法从那样的训练集中进行估计，种类的后验概率也无法被估计出来。
Moreover, as class density estimates cannot be derived for such a training set, class posterior probabilities cannot be estimated.
更进一步地，因种类分布密度无法从那样的训练集中进行估计，种类的后验概率也无法被估计出来。
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