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Pattern recognition dictionary generation apparatus, a pattern recognition device, and pattern recognition for the dictionary generation method

机译:模式识别字典生成装置,模式识别装置以及用于字典生成方法的模式识别

摘要

PROBLEM TO BE SOLVED: To maintain precision of recognition processing, and to quicken the recognition processing in pattern recognition processing.;SOLUTION: A dictionary generation device for pattern recognition is provided with: a pattern input part; a feature extraction part for extracting n-dimensional features; a feature selection dictionary generation part for generating a feature selection function for converting the n-dimensional features into m-dimensional features, and for storing it in a storage medium as a dictionary for feature selection; an identification function generation part for generating a detail identification function for calculating similarity of a pattern, and for storing it in the storage medium as a dictionary for identification; and an identification function primary section extraction part for generating a large classification feature selection function for converting the m-dimensional features into L-dimensional features on an L-dimensional feature space as the subspace of the n-dimensional feature space and the subspace of the m-dimensional feature space, and for generating large classification identification functions by converting detail identification functions as functions on the L-dimensional feature space, and for storing the large classification feature selection functions as the dictionary for feature selection, and for storing the large classification identification functions as the dictionary for identification in a storage medium.;COPYRIGHT: (C)2011,JPO&INPIT
机译:解决的问题:为了保持识别处理的精度,并加快模式识别处理中的识别处理。特征提取部分,用于提取n维特征;特征选择字典生成部分,用于生成将n维特征转换为m维特征的特征选择功能,并将其作为用于特征选择的字典存储在存储介质中;识别功能生成部,用于生成用于计算图案的相似度的详细识别功能,并将其作为用于识别的字典存储在存储介质中;识别功能主要部分提取部分,用于生成大型分类特征选择功能,以将m维特征转换为L维特征空间的子空间作为n维特征空间的子空间和子空间。 m维特征空间,用于通过将详细标识函数转换为L维特征空间上的函数来生成大分类识别函数,并存储大分类特征选择函数作为用于特征选择的字典,并用于存储大分类标识用作存储介质中标识的字典。;版权所有:(C)2011,JPO&INPIT

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