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   annotation method 在 计算机软件及计算机应用 分类中 的翻译结果: 查询用时:0.417秒
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annotation method
相关语句
  标注方法
    Ergonomics Research on Color Bar-based Affective Annotation Method Implementing on Media Player
    基于彩色条的情感标注方法及其在媒体播放器上的工效学应用研究
短句来源
    This method overcomes the limit of the traditional statistical based semantic annotation method. It improved the accuracy and efficiency of image semantic annotation.
    该方法克服了传统的基于统计的语义标注方法效率低、准确率低的缺点,有效提高了图像语义标注的准确率和效率。
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  “annotation method”译为未确定词的双语例句
    The framework direct the annotation process using the principle of max knowledge gain, not random annotation method, in order to gain better retrieval result in the same number of annotated images.
    该框架采用最大增益的原则对图像的注释过程进行指导,而不是盲目地随机注释,以便在注释相同数目图像的情况下,取得尽量好的检索结果。
短句来源
    Our automatic image annotation method employs machine learning and statistical learning technologies.
    在标注图像库的支持下,我们提出了一种新的图像语义自动标注算法。
短句来源
    Considering this, we proposed a color bar based affective annotation method and applied it to a media player, EmoPlayer.
    该方法通过一个彩色条对影片片断中主要角色的情感信息进行标注,并将其安置在一个媒体播放器(“EmoPlayer”)的进度滑动条上方。
短句来源
    GO(Gene Ontology)is a standardized Bioinformatics Ontology,which is widely used to annotate Gene Database. However,such annotation is far from complete due to the structural defect of GO and the manual annotation method in most cases together with some undiscovered attributes of genes.
    GO(GeneOntology)是个标准化的生物信息本体库,被广泛地用来注释基因数据库,然而由于GO结构设计上的缺陷以及目前对基因数据库注释方法多采用手工方式,再加上基因的许多特性尚未发现,使得这种注释还不完全。
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  annotation method
A vision based query interface annotation method is used to relate attributes and form elements in form-based web query interfaces, this method can reach accuracy of 82%.
      
The interface annotation work combines automatic annotation method and user anticipation to achieve much higher accuracy.
      
The annotation method was also evaluated on a word sense disambiguation problem.
      
The annotation method with OCL constraints allows us to specify parameters, variables, and complex functions.
      
Thus, we suggest using our annotation method in conjunction with supervised methods in order to provide maximal coverage and specificity.
      
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GO(Gene Ontology)is a standardized Bioinformatics Ontology,which is widely used to annotate Gene Database.However,such annotation is far from complete due to the structural defect of GO and the manual annotation method in most cases together with some undiscovered attributes of genes.In this paper,we try to get the underlying relationship between gene and gene ontology through the machine learning method of Probabilistic Decision Tree,so as to predict the annotation of gene ontology,i.e.the...

GO(Gene Ontology)is a standardized Bioinformatics Ontology,which is widely used to annotate Gene Database.However,such annotation is far from complete due to the structural defect of GO and the manual annotation method in most cases together with some undiscovered attributes of genes.In this paper,we try to get the underlying relationship between gene and gene ontology through the machine learning method of Probabilistic Decision Tree,so as to predict the annotation of gene ontology,i.e.the unknown attributes of gene.In this way,gene database administrators can be guided to complete and modify the ontology annotation and biologists can be guided to design specific experiments.The MGI gene database is used to test the precision of our prediction and the experimental results show that our method achieves a higher precision.

GO(GeneOntology)是个标准化的生物信息本体库,被广泛地用来注释基因数据库,然而由于GO结构设计上的缺陷以及目前对基因数据库注释方法多采用手工方式,再加上基因的许多特性尚未发现,使得这种注释还不完全。该文尝试用概率决策树的方法来学习得到基因和GO本体的内在联系,进而预测基因的本体注释情况,也就是预测基因的未知特性,这样就可以引导基因数据库管理员去完善,修正基因数据库的本体注释,并指导生物学家有针对性地设计试验。作为一个应用,用MGI基因数据数据库做试验,分析表明用该方法得到的预测结果准确性比较高。

 
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