专利汇可以提供Pattern recognizing method专利检索,专利查询,专利分析的服务。并且PURPOSE: To improve recognizing performance by defining each mean value of a multi-dimensional normal distribution function which is statistically obtained from the learning data as a key vector when the emerging probability density distribution of the feature values of all input patterns is expressed in the weighted linear sum of plural multi-dimensional normal distribution function.
CONSTITUTION: The symbol output probability of a hidden Markov model is calculated to each frame of an input pattern as the product sum between the discrete symbol output probability set to each code vector set previously and the function value of each frame set to plural multi-dimensional normal distribution weight function where each code vector is defined as the mean value. In this case, it is supposed that the emerging probability density distribution of the feature values of all input patterns is shown in the weighted linear sum of plural multi-dimensional normal distribution functions, the mean value of each multi-demensional normal distribution function which is obtained statistically from the learning data is used as each code vector. As a result, the symbol output probability is calculated from a code book where the emerging probability density distribution of the feature value of an entire input pattern is reflected. Thus the pattern recognizing performance is improved.
COPYRIGHT: (C)1991,JPO&Japio,下面是Pattern recognizing method专利的具体信息内容。
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