Chinese translation for "特征项"

feature item

Related Translations:

情感特征:  affective characteristics

行为特征:  behavior characteristics

谈论特征:  describing features

外部特征:  external characteristic

特征穿孔机:  tag marker

边界特征:  boundary characteristic

两性特征:  ambisexualityambosexualityamphoteric character

特征属性:  feature attribute

编辑特征:  edit feature

数字特征:  numerical characteristics

Example Sentences:

	1.	Improvement in text categorization lies not in algorithm of classing model , hut on the fundamental element : integrated and independent feature of text representation 摘要文本分类的进一步改进除了算法方面，应该还立足于影响文本分类最底层、最根本的因素：文本表示中的特征项，提高特征项的完整独立程度。
	2.	Among them , document representation is the foundation of information retrieval technologies . document representation includes document feature item extraction and weight calculation of document feature item 其中文档表示是信息检索技术的基础，而文档表示包括文档特征项的抽取和文档的特征项权重的统计。
	3.	For text classification based on rs theory , a decision table is created with the weights of text characteristic terms discretized as the rules " condition attributes and the classes of texts as decision attributes 基于粗糙集理论的文本分类算法中，将文本特征项的权值作为规则的条件属性，文本所属的类别用作决策属性，构造决策信息表。
	4.	This trained bp neural net can output the weight of document feature item if its frequency is inputted , therefore the net can represent the document feature . from tests , this method is proved that it c an applied practicably and simply , and have high precision 本文设汁并训练出一个适合的bp网络，给出一个文档的特征项出现频率能计算出特征项在该文档中的权重，从而表示出文档特征。
	5.	3 . advancing a method to calculate weight of document feature item based on bp neural net the key technologies of information retrieval system are the followings : representation of document and user query ; query matching strategy ; correlation calculation of matching result 提出一种基于bp神经网络统计文档特征项权重的方法信息检索系统的核心技术主要包括三个方面的内容：文档与用户查询的表示：查询匹配策略；匹配结果的相关度计算。
	6.	( 2 ) the influence to classification result is highly effected by using different classifier , for example , the center - vector algorithm obtains better classification results than other two algorithms . with the character feature , the average recall is 80 . 73 % , and the average precision is 82 . 94 % , and with the chinese - word feature , the average recall is 83 . 6 % , and the average precision is 85 . 97 % . different corpuses influence the classification result . for example , the average recall is 89 . 31 % and the average precision is 88 . 33 % , by using the news web pages as corpus from the web site " www . sina . com . cn " , which adopt the center - vector algorithm to structure classifier and select chinese - word as feature 对三种分类器分别以字、词为特征进行分类测试、分析发现：使用相同的分类算法，用词作为特征项，比以字作为特征的分类效果好；用不同的算法构造分类器对分类效果的影响很大，如中心向量算法在字、词特征下的分类效果优于其他两算法；在以字为特征的情况下，该算法的平均查全率80 . 73 ，平均查准率82 . 94 ；在以词为特征的情况下，该算法的平均查全率83 . 6 ，平均查准率85 . 97 ；选用语料不同对分类效果也有影响，如用新浪网( www . sina . com . cn )网页语料进行测试，使用中心向量法分类器和词作为特征的情况下，平均准确率为89 . 31 ，平均查全率为88 . 33 。
	7.	Aimed at scarcities of current means to calculate document feature item weight , using vsm for reference , this paper advances a method to calculate weight of document feature item based on bp neural net . bp neural net is applied broadly for its simple structure and working stabilization 本文针对现存的文档特征项基于internet的信目、检索若干问题的研究权重统计方法的不足，结合向量空间模型表示文档特征的方法，提出利用神经网络技术对文档的文档特征权重进行估计的方法。
	8.	In the information gathering research , the paper uses " redundancy webpage filtering system " to show how to resolve the reconstruction problem in the information gathering process . the paper mainly discussed how to reduce the time complicacy and space complicacy of the filtering system 在信息资源建设方面，本文用“冗余网页过滤系统”来说明如何解决资源建设中的重复建设问题，在研究中主要针对算法的时间复杂度和空间复杂度进行了优化，找到了表达一篇文档的最优特征项个数数值，在确保正确率的基础上加快过滤速度。
	9.	Wbs has not only implemented the web log mining function , but also put forward a method to supply the needs of personalization service . this method was based on the technique of web usage mining and web content mining , hi this system bookmarks and users are represented by vector on feature space and managed in a uniform manner by the engine 结合系统特点? ? bookmark服务，系统不仅实现了日志挖掘功能，并提出了一种结合web用法挖掘和web内容挖掘的方法，即将bookmark和用户以基于特征项空间的向量形式统一进行管理，采用聚类( clustering )的方法根据匹配度来为用户提供个性化服务。

Similar Words：

"特征线系" Chinese translation, "特征相互作用管理" Chinese translation, "特征相量" Chinese translation, "特征相位" Chinese translation, "特征相移" Chinese translation, "特征向量" Chinese translation, "特征向量抽取" Chinese translation, "特征向量分析" Chinese translation, "特征向量分析法" Chinese translation, "特征向量矩阵" Chinese translation