像谷歌和脸书拥有的训练集是私有的。
Training sets like the ones Google and Facebook have are private.
但是这些都是可以通过训练集估计求得。
但是，学习训练集表现良好并不一定是件好事。
But learning the training set well is not necessarily the best thing to do.
聚类不需要训练集，但准确率较低。
The training set is not needed in clustering but the accuracy is lower.
支持向量机由核函数与训练集完全刻画。
It is completely characterized by kernel function and training set.
你可以这样设想，并不是所有的训练集的输入都分类正确。
As you might imagine, not all training sets have the inputs classified correctly.
我使用相同的国防军从训练集进行交叉验证吗？
Do I use the same idf from training set to perform cross validation?
训练集有多大？
首先提取出文本训练集的特征词，建立特征向量空间模型。
Firstly character words of training documents are extracted, vector space model is constructed.
以此方法对样本集进行有效扩充，得到新的随机扩展训练集。
According to this method, a random expanded training set is obtained.
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。
We've already talked a bit about the fact that algorithms may over-fit the training 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。
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.
实验结果表明，训练集比例至少为50%时才能使分类错误率达到相对平稳。
Results show that proportion of train-set needs to be at least 50% for a comparatively stable error-rate.
通过比较被测试图像的模型参数和训练集图像的模型参数确定被测试图像的类别。
Parameters of the model about testing image and about training images are compared to classify the testing image.
结果表明，在训练集样本数据较少时，广义回归神经网络的预测准确度仍然很高。
The results showed that the prediction accuracy is satisfied, even though there are a few data in training sets.
它在包含少量有标签样本的训练集和大量无标签样本的测试集上，具有良好的效果。
They are more efficient than inductive SVMs, especially for very small training sets and large test sets.
在所有的数据集(包括训练集，验证集和测试集)中，一个条目都至少有20个打分。
An item has at least 20 ratings in the total dataset (including train, validation, and test sets).
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。
In selecting vectors, the algorithm also USES the class label of training set and the judgment information of class mean vector.
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。
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.
提出了一种用无监督聚类算法指导文本分类的方法，以解决没有训练集的文本分类问题。
We propose a method, use Unsupervised text Clustering algorithms (UTC) to guid text classification, so as to deal with text classification without training set.
核函数生成既考虑了训练集样本自身的类别因素，又考虑了错分样本与邻近类别的关系。
Whether or not a kernel function comes into being depends on the relationships between some misclassification patterns and their neighbor classes.
基于概率的算法只考虑了训练集语料的概率模型，对于不同领域的文本的处理不尽如人意。
And the probabilistic methods those consider the probabilistic model of the training set only also do a bad job on the texts of a specific domain.
大多数流形对齐算法只能给出了训练集上的预测值，而没有给出整个数据空间上的映射关系。
Most of manifold alignment algorithms can only give the predictive value of the training set instead of producing a mapping defined everywhere.
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；
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.
基于粗糙集理论的数据挖掘技术，通过数据训练集所训练得到的算法模型能够有效用于疾病诊断。
The data mining technology based on the rough set theory can be trained through the training data 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。
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.
利用粗糙集理论，通过对训练集的学习，构造分类规则，对支持向量机反馈后的结果再次进行处理。
By using rough set theory, this paper structures classification rules and processes the support vector machine feedback results with learning the train set.
对于SVM，本文给出了一个核函数选择与参数调整的算法，它能够对给定训练集得到最优的参数调整。
For SVM, in this paper, a kernel function selection and parameter adjustment algorithm are presented. It can get optimal parameter adjustment in a given training set.
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。
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。
These inputs, often called the "training set", are the examples from which the agent tries to learn.
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