权重矩阵的英文

• weighting matrix

• "权重"英文翻译    weight
• "矩阵"英文翻译    matrix; array
• "概率权重矩法" 英文翻译 :    probability weight－moment method
• "重矩" 英文翻译 :    shigenori
• "权重" 英文翻译 :    weight◇权重法 method of weighting
• "加权，权重" 英文翻译 :    wweight
• "权重的" 英文翻译 :    weighted
• "权重东" 英文翻译 :    kwon joong dong; kwon joong-dong
• "权重法" 英文翻译 :    method of weighting; methodofweighting
• "权重集" 英文翻译 :    weight sets
• "权重谱" 英文翻译 :    weight spectrum
• "双权重" 英文翻译 :    double weight
• "s矩阵" 英文翻译 :    s matrix; s-matrix
• "t矩阵" 英文翻译 :    t matrix
• "矩阵" 英文翻译 :    [数学] matrix; array
• "风险权重" 英文翻译 :    risk rated ratio
• "高斯权重" 英文翻译 :    gaussian weight
• "模糊权重" 英文翻译 :    fuzzy weight
• "内插权重" 英文翻译 :    interpolation weight
• "权重叠加" 英文翻译 :    weighted superposition
• "权重叠架" 英文翻译 :    weighted superposition
• "权重函数" 英文翻译 :    weight function; weighting function
• "权重计数子" 英文翻译 :    weight enumerator; weightenumerator
• "权重滤波器" 英文翻译 :    weighting filter
• "权重配位数" 英文翻译 :    coordination weighted number

• A new pcwm for e . coli - k12 promoter prediction
  基于位置关联权重矩阵的大肠杆菌启动子预测
• The results show that the gwm provides higher accuracy
  结果表明，分组权重矩阵法的识别效果更为精确。
• Pwm , position weight matrix
  结合位置权重矩阵
• In this paper , grouped weight matrix ( gwm ) method is proposed for splice site recognition
  摘要提出识别剪切位点的分组权重矩阵方法。
• The gwm model for splice signals is a mixture of weight matrix models ( wmm ) with unknown number of components
  分组权重矩阵是一个子总体数未知的混合权重矩阵模型。
• Firstly , the notion of the form as a = > b [ s % , c % ] is researched and some algorithms are discussed . an algorithm named a progressive refinement approach to spatial data mining is discussed in detail . and a new thought of mining spatial association rule based on spatial data cube is brought forward
  阐述了a = > b [ s ， c ]形式的空间关联规则的基本概念和算法，详细研究了一种逐步求精的空间关联规则挖掘算法的实现；提出一种基于空间数据立方体的空间关联规则挖掘的新思路；将空间统计分析引入空间关联规则挖掘领域，研究了空间权重矩阵、空间自相关、空间关联等的度量函数，并利用空间统计分析技术发现空间相关关系和空间关联规则。
• In this thesis , an algorithm based on multiple features for recognition of escherichia coli promoter was proposed . firstly , word frequency method was utilized to extract the content ’ s information of a given sequence , and position weight matrix and hidden markov model were applied to analyze the information on structure , and then this information was input into a classifier
  本文提出了一种基于多特征的大肠杆菌启动子判别算法,即通过词频分析获得序列的组成特征,利用位置权重矩阵( pwm )和隐马尔科夫模型( hmm )获得序列的结构特征,然后输入到一个分类器中进行分类。