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Training of a Hidden Markov Model using training data vectors and a nearest neighbor clustering method based on condition parameters used to describe the Hidden Markov Model

机译:使用训练数据向量和基于用于描述隐马尔可夫模型的条件参数的最近邻聚类方法训练隐马尔可夫模型

摘要

Method for computer-based training of a Hidden Markov Model (HMM) in which condition parameters are formed with which the HMM can be described, training data vectors are used to carry out a k-nearest neighbor clustering method based on the condition parameters, whereby the condition parameters are matched to the training data vectors, so that a trained HMM is formed. Independent claims are also included for the following:- (a) a device for training an HMM; (b) a computer program element for use on a computer for training an HMM and; (c) a computer readable storage medium for storing a computer program for training an HMM.
机译:基于计算机的训练隐马尔可夫模型(HMM)的方法,其中形成了可描述HMM的条件参数,训练数据矢量用于基于条件参数执行k最近邻聚类方法,从而条件参数与训练数据向量匹配,从而形成训练后的HMM。还包括以下方面的独立权利要求:-(a)训练HMM的设备; (b)用于训练HMM的计算机上使用的计算机程序元素;以及(c)用于存储用于训练HMM的计算机程序的计算机可读存储介质。

著录项

  • 公开/公告号DE10302101A1

    专利类型

  • 公开/公告日2004-08-05

    原文格式PDF

  • 申请/专利权人 INFINEON TECHNOLOGIES AG;

    申请/专利号DE2003102101

  • 发明设计人 TSCHIRK WOLFGANG;STERZ WALTER;

    申请日2003-01-21

  • 分类号G10L15/14;

  • 国家 DE

  • 入库时间 2022-08-21 22:43:35

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