训练样本集的英文

• training sample set

• "训练"英文翻译    train; drill; manage; practi ...
• "样本"英文翻译    sample book; specimen; advan ...
• "集"英文翻译    gather; assemble; collect
• "训练样本" 英文翻译 :    training sample
• "样本集" 英文翻译 :    sample set
• "训练样区" 英文翻译 :    training field
• "样本集合" 英文翻译 :    sample set
• "正样本集" 英文翻译 :    positive sample set
• "国际客票样本集" 英文翻译 :    collection of model international tickets
• "结构完整样本集" 英文翻译 :    structurally-complete sample set
• "结构完善的正样本集" 英文翻译 :    structural complete positive sample set
• "结构完整的正样本集" 英文翻译 :    structurally complete positive sample set
• "与样本集兼容的文法" 英文翻译 :    grammar compatible with sample set
• "本集" 英文翻译 :    mototsume; samhita
• "样本" 英文翻译 :    sample book; specimen; advanced copy; sample; muster; scantling; instance; statistics 字体样本 type specimen book
• "标本集" 英文翻译 :    specimen collections
• "复本集" 英文翻译 :    replica set
• "基本集" 英文翻译 :    base set; baseset; basic set; fundamental set
• "干燥标本集" 英文翻译 :    herbaria; herbarium
• "宫本集团" 英文翻译 :    miyamoto clique
• "基本集合论" 英文翻译 :    basic set theory
• "植物标本集" 英文翻译 :    herbarium
• "资本集约" 英文翻译 :    capital intensive
• "资本集约的" 英文翻译 :    capital intensive
• "资本集约度" 英文翻译 :    capital intensity

• The experimental results indicate that it is easy to be realized , can save the calculating cost and improve the constringency speed
  试验结果表明，用粒子群算法来训练样本集具有容易实现、节省计算成本和提高收敛速度等优点。
• The numbers of the training sample of the networks are 37 , 32 , 27 , 22 and the numbers of the test sample are 5 , 10 , 15 , 20 , respectively , and the relative errors of the predications are less than 3 % . it is shown that the rough network is accurate and available for prediction of stock market
  训练样本集的天数分别是37天， 32天， 27天和22天，预测样本集的天数分别是后5天， 10天， 15天和20天，预测的相对误差都小于3 。
• Another understanding about intrusion detection is viewing machine learning as a searching process , that is to say , intrusion detection is in essence the searching or approximation issue of intrusion rules in accordance to established searching strategy . after some concerned
  在基于遗传学习的入侵检测研究中,把机器学习看作一个搜索过程,即入侵检测可视为基于训练样本集,按照既定的搜索策略对入侵规则的搜索或逼近问题。
• Firstly , the samples are divided into several sample set , every sample set is represented by one modular e - hmm , the modular e - hmm parameters are obtained through doubly embedded viterbi training algorithm , and the necessary temporary parameters in the parameter re - estimating process are also saved for the use of next step
  该方法对训练样本中不同训练样本集，都通过训练算法产生相应的模型参数模块，训练结束后对不同的模型参数模块合成，得到最终的模型。
• 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。
• Some new ideas are proposed in this thesis based on svm and ica : firstly , a modified svm method based on posteriori probability theory is given , which makes the classification super plane corrected from the original one . a better classification result is obtained without finding the best quadric optimization algorithm and large scale training datasets are reduced to small scale training datasets at the same time . secondly , ica is applied to the preprocessing period of the recognition character images for purpose of feature extraction and dimension reduction
  本文在系统研究svm和ica的基础上提出了以下新的观点：其一是采用了引入后验概率的修正svm方法，它在原分类超平面的基础上不断修正分类超平面，提高分类正确率，从而避免了寻找最优二次规划的麻烦，同时将大规模训练样本集化为小规模训练样本集；其二是应用独立分量分析ica对需要进行识别的字符图像预处理，提取字符特征，降低输入数据的维数，从而可以为下一步的svm识别过程提供好的数据集，用以提高识别率和识别速度。
• After researching the application of artificial neural network to fault diagnosis of transformer , the back propagation algorithm is improved observably . inducting chaos dynamics principium to create chaos neural network back propagation algorithm and using the pretreatment of training sample , the emulation program show that this method is effective
  本文深入研究了人工神经网络在变压器故障诊断中的应用，对目前最常采用的bp算法做出了较大改进，在原有算法中引入混沌动力学原理构造了混沌神经网络bp算法，并提出了训练样本集的预处理方法，在仿真训练中取得了满意的效果。