training data set造句

• Training data set
  培训数据集
• In the " chinese english physical websites classification system " , the paper analyses the taxonomy and train data set and gets some useful experience on information management
  在“中英文物理网站分类系统”案例中，本文通过对分类体系和训练数据进行分析，取得了采用文本分类技术来进行信息资源管理的一些经验。
• If methods use a training data set with correct classifications for learning specific predictive patterns , they are called “ supervised ” . if we just use the data itself to and internal structure , the method is called “ unsupervised ”
  如果用来建立模型的训练集合中的每个样本已经有了明确的类别属性,那么在这样的数据集上建立模型的过程就是有指导学习。
• Thus the scale of the training data set is reduced greatly and the training speed of svm is improved enormously . because the decision boundary of svm is only determined by support vectors , the classification accuracy is almost preserved when other samples are omitted
  在svm学习之前，首先剔除训练样本集中距离判决边界远的样本，选取靠近判决边界的样本构成有效训练样本集，然后用svm对有效样本集进行学习，这样大大降低了训练样本集的规模，提高了svm的学习速度。
• By applying ripper algorithm to a great lot data sets in intrusion detection fields , we found that the inductive ability of this traditional classification algorithm could be greatly damaged by the lack of negative examples in training data sets . given the prevalence of lack of negative examples ( which cover some intrusion types ) in the audit data we can offer , this limitation was almost lethal
  在大量的入侵检测环境下的数据上应用ripper分类算法的结果显示，这一传统分类算法强大的归纳能力对于入侵检测环境下反例缺乏(我们提供的审计数据不可能函概所有的入侵类型)的数据集不能很好地适应。
• At the same time , this paper puts forward a validity function for judging clustering in order to lead us to use it in k - nearest neighbor classification ; then introduces " generalization capability of a case " to k - nearest neighbour . according to the proposed approach , the cases with better generalization capability are maintained as the representative cases while those redundant cases found in their coverage are removed . we can find a new less but almost complete training data set , consequently reduce complexity of seeking near neighbour
  针对k值的学习，本文初步使用了遗传算法选择较优的k值，同时总结了一种聚类有效性函数，数值实验证实了其有效性，旨在指导应用于k -近邻分类中；然后还将“扩张能力”的概念引入k -近邻算法，根据训练集例子不同的覆盖能力，删除冗余样本，得到数量较小同时代表类别情况又比较完全的新的训练集，从而降低查找近邻复杂性。