They can, however, be estimated from the training set.
但是这些都是可以通过训练集估计求得。
But learning the training set well is not necessarily the best thing to do.
但是，学习训练集表现良好并不一定是件好事。
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。
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。
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。
How large is your training set?
训练集有多大？
It is completely characterized by kernel function and training set.
支持向量机由核函数与训练集完全刻画。
According to this method, a random expanded training set is obtained.
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。
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。
The experimental data obtained were divided into training set and testing data.
把试验获取的数据整理分为训练样本集和测试样本集。
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。
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。
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。
Then, the relationship between clustering regions and semantic concepts is established according to the labeled images in the training set.
然后，根据训练集中样本图像的标注情况建立图像区域与语义关键字的关联。
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。
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。
Most of manifold alignment algorithms can only give the predictive value of the training set instead of producing a mapping defined everywhere.
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。
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。
The algorithm is quite simple, especially, on the certain condition, it can match all the input-output pairs in the training set to any given accuracy.
这种算法非常简单，且在一定条件下，能将样本集合中的所有输入、输出数据对拟合至任意精度。
Based on the equivalence between the original training set and the newly added training set, a new algorithm for SVM-based incremental learning was proposed.
基于原训练样本集和新增训练样本集在增量训练中地位等同，提出了一种新的SVM增量学习算法。
The well-designed training set was verified by simulation test. The simulation result proved that the model was correct and the diagnosis method was feasible.
仿真验证试验结果表明所设计的基于人工神经网络的电力电子逆变电路的故障诊断系统是可行的。
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。
Comparing with the nearest neighbor method, the presented method needs less cost and memory space as the representative samples are only a little part of the training set.
而支撑点仅占全部训练样本的一部分，所以相比最近邻方法具有较小运算量和存储量的优点。
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；
It showed that the correct classification rate reached 95% in training set including 68 patterns, and the correct prediction rate was about 90% in prediction set including 20 patterns.
结果表明，对学习集中68种化合物的正确分类率达到95%，对其余20种化合物的正确预报率达到90%。
The effect of training set size on the error of neural network model is analyzed. The results show that the model has reasonable accuracy when the training set size is not less than 14.
分析了训练样本集的大小对模型误差的影响，指出在训练样本不少于14个的情况下，模型具有较高的预测精度。
Secondly, new B3LYP functional is constructed combined artificial neural network (ANN) with enlarged training set and the new B3LYP functional is adopted to calculate the molecular energies.
其次，采用人工神经网络方法（ANN）和扩大训练基组构建新B3LYP泛函，并将新泛函用于分子能量的计算。
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)。
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)。
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