文本分类中基于核的非线性判别

针对文本分类问题中的特征降维问题，改进最大散度差鉴别准则，引入核变换作为前处理，使最大散度差鉴别准则可适用于更广泛的文本分类情形. 提出一种基于核的非线性鉴别方法用于文本特征抽取. 借助于核变换解决了散度差准则在用于文本分类时线性可分性较差的问题. 在最低限度减少信息损失的前提下实现了特征维数的大幅度减缩. 文本分类试验结果表明，这种非线性方法与无核的最大散度差方法相比，F1值提高了4.7%, 具有明显的效率上的优势.

Kernel-Based Nonlinear Discriminant Method in Text Classification

To achieve feature reduction in text categorization, the scatter difference criterion is improved to satisfy a broad range of text categorization problems using kernel commutation in the pre-treatment. A kernel-based nonlinear method is proposed to extract features. By kernel commutation, the stylebook categorization problem is solved with less linear separability. Dimension of the feature space is significantly reduced without incurring excessive information loss. Experiments show that performance of the proposed method is better than maximal scatter difference with an efficiency improvement of 4.7% for the value of F1.