应用特征选取和模式聚合理论以降低特征空间维数。
This paper employs feature selection theory and pattern aggregation theory to reduce feature space dimension.
首先，数均分子量随转化率的增加线性增长，且与活性聚合理论计算分子量相符；
The molecular weight of obtained polystyrene increases linearly with the overall conversion and agrees with the theoretically predicted molecular weight.
首先采用模式聚合理论进行特征抽取，将对文本分类具有相似贡献的特征合并，映射为新的特征空间。
Firstly, using pattern aggregation theoretical models to extract features, merge the features which have the similar contributions to text classification, then a new mapping feature space is formed.
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