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Scheme and model adaptation in the case of pattern recognition is based on taylor expansion

机译:模式识别情况下的方案和模型自适应基于泰勒展开

摘要

A model adaptation scheme in the pattern recognition, which is capable of realizing a fast, real time model adaptation and improving the recognition performance. This model adaptation scheme determines a change in a parameter expressing a condition of pattern recognition and probabilistic model training between an initial condition at a time of acquiring training data used in obtaining a model parameter of each probabilistic model and a current condition at a time of actual recognition. Then, the probabilistic models are adapted by obtaining a model parameter after a condition change by updating a model parameter before a condition change according to the determined change, when the initial condition and the current condition are mismatching. The adaptation processing uses a Taylor expansion expressing a change in the model parameter in terms of a change in the parameter expressing the condition.
机译:一种模式识别中的模型自适应方案,能够实现快速,实时的模型自适应,并提高识别性能。该模型自适应方案确定表达模式识别和概率模型训练的条件的参数在获取用于获得每个概率模型的模型参数的训练数据时的初始条件与实际时的当前条件之间的变化。承认。然后,当初始条件和当前条件不匹配时,通过根据确定的变化通过更新条件改变之前的模型参数来获得条件改变之后的模型参数,来适配概率模型。适应处理根据表示条件的参数的变化,使用表示模型参数的变化的泰勒展开。

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