Invention Grant
US07693683B2 Information classifying device, information classifying method, information classifying program, information classifying system 失效
信息分类装置,信息分类方法,信息分类程序,信息分类系统

  • Patent Title: Information classifying device, information classifying method, information classifying program, information classifying system
  • Patent Title (中): 信息分类装置,信息分类方法,信息分类程序,信息分类系统
  • Application No.: US11791705
    Application Date: 2005-11-17
  • Publication No.: US07693683B2
    Publication Date: 2010-04-06
  • Inventor: Masayoshi Ihara
  • Applicant: Masayoshi Ihara
  • Applicant Address: JP Osaka
  • Assignee: Sharp Kabushiki Kaisha
  • Current Assignee: Sharp Kabushiki Kaisha
  • Current Assignee Address: JP Osaka
  • Agency: Birch, Stewart, Kolasch & Birch, LLP
  • Priority: JP2004-340723 20041125; JP2005-147048 20050519
  • International Application: PCT/JP2005/021095 WO 20051117
  • International Announcement: WO2006/087854 WO 20060824
  • Main IPC: G06F17/18
  • IPC: G06F17/18 G06F17/30 G06K9/62
Information classifying device, information classifying method, information classifying program, information classifying system
Abstract:
An information classifying device calculates, for a plurality of populations containing pieces of sample information, evaluation distance between a center of gravity of the pieces of sample information belonging to each population and a piece of sample information as an object of classification (object sample), calculates statistical information such as mean, variance and standard deviation of the evaluation distance for each population, evaluates the evaluation distance of the sample information to the population based on the evaluation distance and the statistical information and evaluates degree of assignment relevancy of the object sample to the population, determines to which population the object sample is to be assigned in accordance with the degree of assignment relevancy, and assigns the object sample to the population. Evaluation distance between the center of gravity of each updated population and the object sample belonging to each population is calculated. If the degree of assignment relevancy to every population is out of a prescribed range, a new population is formed, and the object sample is assigned to the new population. Thus, autonomous and stable classification of object sample to a population becomes possible.
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