training data造句

• Also , the act of causing a mining model to evaluate training data
  视为一个单元的一组相关字段（列）信息。
• Trains data mining models
  为数据挖掘模型定型。
• That is , it writes out all the training data to your local hard disk
  也就是说，所有定型数据都将写在本地硬盘上。
• The case table includes the training data that you will use to train the mining model
  事例表包括要用来为挖掘模型定型的定型数据。
• Training data set
  培训数据集
• Sample cases may be generated and may not actually exist in the training data
  可能生成样本事例，但其可能并不实际存在于定型数据中。
• Statement to remove all the trained data from a mining model or mining structure
  语句可以从挖掘模型或挖掘结构中删除所有定型的数据。
• Unlike the ordinary weights of evidence using point training sets , the new method involves training data as a fuzzy set
  通过适当的变换也可以把点训练层转换为模糊集合。
• The experiments show that this algorithm can deal with sparse train data with rather high accuracy
  实验结果表明，该算法在训练集较小的情况下，算法可以具有较好的性能。
• Specify the data source view or cube dimension that contains the training data on which to build the model
  指定包含据以生成模型的定型数据的数据源视图或多维数据集维度。
• The decision tree ( or rules ) used for rail deformation detection was generated by learning the train data
  通过对训练数据的学习,生成用于轨道故障判决的决策树(或者规则) 。
• But the designed robust control method needs a lot of trained data of neural network , so the operation speed is limited
  但此方法需要大量的神经网络训练数据，运算速度受到限制。
• Point training data can be converted to a fuzzy set , therefore the new method can predict both point events and area - events
  因此，该方法可以对面事件、点事件和线事件进行评价和预测。
• When both evidence and training data are fuzzy sets , the new method acts as a dual fuzzy weights of evidence method
  该方法可以处理训练层和证据层均为模糊集合的情况，被称为双重模糊证据权方法。
• Your analysis will focus on the impact of context representation and the features of training data on the quality of generated clusters
  你的分析重点是上下文表示方法和训练数据的特征对生成的类的质量的影响。
• This metric is optimal in the sense of global quadratic minimization , and can be obtained from the clusters in the training data in a supervised fashion
  这个度量导入二次最佳化问题的解，将训练样本类结构的倾斜最小化。
• The optimization process is characterized by the properties of self - organization and self - learning , and prior knowledge or training data are not needed
  设计过程无需系统的先验知识和训练数据，具有自组织、自学习的特点。
• Fnn efficiently maps the complex non - linear relationship of training data for its automatic learning , generation and fuzzy logic inference
  模糊神经网络具有很强的自学习、泛化和模糊逻辑推理功能，可以有效地映射出训练数据之间复杂的非线性关系。