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fdod函数
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  “fdod函数”译为未确定词的双语例句
     therefore their predictive accuracies are limited. In this paper, a new prediction approach based on the subsequence distribution and the FDOD function is proposed. It is superiors to the former methods for that it includes the information of residue orders.
     本文结合子序列分布和FDOD函数,给出了一种新的蛋白质结构类预测方法,和现有的预测方法相比,它考虑了氨基酸残基的排列顺序,从而显著提高了预测精度,与张春霆院士的最新结果相比,两类检验的总预测精度分别提高了3.3%和5.3%。
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     It is also concluded that the length of subsequences affects the predictive results, especially for the proteins with mixed secondary structures. Single data set tests give the predictive accuracy of 73%.
     基于FDOD函数和氨基酸组成,本文构造了一种新的蛋白质亚细胞定位预测方法,预测结果与支持向量机等方法预测结果进行了比较,对于真核生物蛋白质总预测精度比支持向量机方法得到的结果高2.6%,对于原核生物蛋白质预测结果基本一致。
短句来源
     The performances of the new method and the SVM method are compared.
     5.总结了不同氨基酸序列的特征描述方法,以FDOD函数作为判别函数,比较了
短句来源
     As Kullback-Leibler entropy, this method measures the similarity by measuring the generalized distance between the joint distribution and the distribution associated to the case of complete indepence.
     本文采用另一种度量该距离的方法——信息离散性度量(简称FDOD 函数)方法,与Kullback—Leibler熵相比,FDOD函数具有更多好的数学性质。
  相似匹配句对
     The Lcm-sum Function
     最小公倍数的和函数
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     The icm-sum function
     最小公倍数的和函数
短句来源
     The performances of the new method and the SVM method are compared.
     5.总结了不同氨基酸序列的特征描述方法,以FDOD函数作为判别函数,比较了
短句来源
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This paper is to study the rule of combination in Evidence Theory. Some problems existing in Dempster' s rule of combination are discussed. Based on the theory of Entropy and the Degree of Disagreement Function which measures the information discrepancy, some new rules for combining different mass functions are proposed. When faced with great Dempster - conflicts of different evidences, we could get reasonable results by using these new rules. An application of our new rules of combination to the theory of...

This paper is to study the rule of combination in Evidence Theory. Some problems existing in Dempster' s rule of combination are discussed. Based on the theory of Entropy and the Degree of Disagreement Function which measures the information discrepancy, some new rules for combining different mass functions are proposed. When faced with great Dempster - conflicts of different evidences, we could get reasonable results by using these new rules. An application of our new rules of combination to the theory of decision-making is presented.

本文对证据推理中Dempster融合规则存在的一些问题进行了讨论,利用信息论中的熵函数和度量信息离散度的FDOD函数提出了新的信息融合规则,改进了Dempster规则,在不同证据冲突较大的不合理结果。此外,文章还介绍了如何将新的融合规则应用于决策系统。

>=This paper is to study the rule of combination in Evidence Theory.Someproblems existing in Dempster’s rule of combination are discussed.Based on the theory ofEntropy and the Degree of Disagreement Function which measures the information discrep-ancy,some new rules for combining difierent mass functions are proposed.When faced withgreat Dempster-conflicts of difierent evidences,we could get reasonable results by using thesenew rules.An application of our new rules of combination to the theory of decision.makingiS...

>=This paper is to study the rule of combination in Evidence Theory.Someproblems existing in Dempster’s rule of combination are discussed.Based on the theory ofEntropy and the Degree of Disagreement Function which measures the information discrep-ancy,some new rules for combining difierent mass functions are proposed.When faced withgreat Dempster-conflicts of difierent evidences,we could get reasonable results by using thesenew rules.An application of our new rules of combination to the theory of decision.makingiS presented.

本文对证据推理中Dempster融合规则存在的一些问题进行了讨论,利用信息论中的熵函数和度量信息离散度的FDOD函数提出了新的信息融合规则,改进了Dempster规则,在不同证据冲突较大的不合理结果.此外,文章还介绍了如何将新的融合规则应用于决策系统。 。

>=In this paper, we introduce a new measure of information discrepancy to medical image registration. As Kullback-Leibler entropy, this method measures the similarity by measuring the generalized distance between the joint distribution and the distribution associated to the case of complete indepence. The simulated registration shows that this new method can register images effectively, subvoxel and automatically.

近几年受大家欢迎的基于互信息的配准方法通过最大化两幅图像灰度值的联合概率分布和完全独立时他们的边缘概率分布之积的广义距离来达到图像配准的目的,该距离是用 Kullback-Leibler熵度量的;本文采用另一种度量该距离的方法——信息离散性度量(简称FDOD 函数)方法,与Kullback—Leibler熵相比,FDOD函数具有更多好的数学性质。模拟配准试验表明基于信息离散性度量的配准方法可以对图像进行自动地、有效地、亚像素级地配准。

 
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