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语义空间的英文

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"语义空间"怎么读用"语义空间"造句

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  • semantic space

例句与用法

  • Therefore in the process of color design , when input the abstract words for describing the product color in cad environment , reference color scheme for the product color design will be acquired
    并采用基于约束的求解方法,完成了在语义空间中色彩方案表示和求解的全过程。
  • It includes 3d semantic space and constraining based problem - solving method . the 3d semantic space is built to represent the relationship between the describing semantic word and product color . then , the product color schemes with related abstract sense words is achieved and saved in database
    首先采用定性和定量相结合的方法,比较各抽象语义词汇对人心理感觉影响的差异,建立了抽象语言和色彩关联的三维风格语义空间,为色彩信息的表示及推理奠定了基础。
  • Document clustering techniques have been received more and more attentions as a fundamental and enabling tool for efficient organization , navigation , retrieval , and summarization of huge volumes of text documents . the aim of document clustering is to cluster the documents into different semantic classes in an unsupervised manner
    文本聚类作为一种对大规模文本信息进行有效地组织、导航、检索和概括汇总的关键的、基本的技术而日益受到关注,其主要目的是在语义空间里以无监督的方式将文本集中的文本划分成不同的类。
  • If a document is viewed as a combination of basis topics , and every basis topic is represented by a related vector , then it can be categorized as belonging to the topic represented by its principal vector . thus , nmf can be used to organize document collections into partitioned structures or clusters directly derived from the nonnegative factors
    如果将一篇文档看作是由许多文本主题组合而成,而文本主题与语义空间的特征向量相对应的话,则我们可以直接根据nmf算法所提取的特征向量及相对于特征向量各文档的编码向量将文本集划分成不同的类。
  • Abstract this paper stems from " 863 " project and national natural science fund . based on the theory of industrial design and fuzzy set , using object - oriented technology and semantic grammar technology , a semantic transformation method for product color design is proposed
    本文依据“十五”国家高技术发展计划( 863 )课题项目和国家自然科学基金,以工业设计理论及现代设计方法学为基础,综合应用模糊逻辑、面向对象技术、语义文法等技术方法,提出了一种基于语义空间的产品色彩设计方法。
  • The document space is generally of high dimensionality and clustering in such a high dimensional space is often infeasible due to the curse of dimensionality . so the primary step in document clustering is to project the document into a lower - rank semantic space in which the documents related to the same semantics are close to each other
    基于文本空间的文本聚类因为其具有高维的特征而不容易直接实现,所以文本聚类的首要步骤就是将文本空间的数据投影到较低维的语义空间里,使在文本空间里相邻的数据向量在语义空间里根据某些提取的特征参数而相似。
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