针对这些问题，基于最小化学习误差的增量思想，该文将学习型矢量量化（LVQ）和生长型神经气（GNG）结合起来提出一种新的增量学习型矢量量化方法，并将其应用到文本分类中。
To solve these problems, based on minimizing the increment of learning errors and combining LVQ and GNG, the authors propose a new growing LVQ method and apply it to text classification.
这是因为考虑到类别的关联性，不同的类别对最小化分类误差的贡献是不同的。
The reason is the contributions of different labels to minimizing the classification error are different due to the inherent label correlations.
并通过最小化分类误差准则最大化SVM两类输出值概率分布间的距离。
The proposed scheme also employs the rule that minimizes the error of classifications to maximize the distance of the output distributions of two classes.
并通过最小化分类误差准则最大化SVM两类输出值概率分布间的距离。
The proposed scheme also employs the rule that minimizes the error of classifications to maximize the distance of the output distributions of two classes.
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