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Method of automatically training a classifier hierarchy by dynamic grouping the training samples
Method of automatically training a classifier hierarchy by dynamic grouping the training samples
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机译:通过动态分组训练样本自动训练分类器层次结构的方法
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摘要
The present invention uses dynamic grouping to divide up training samples to train different classification nodes. At the beginning of the training, all samples are in the same group. A clustering process is applied in the feature space of the selected feature vectors with cluster indexes accumulated. The average of all the accumulated cluster indexes is used as the threshold for splitting the samples into two groups. When the splitting criterion is met, samples are split into two groups based on their similarity in the feature space.
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