基于本体的文本分类研究综述_管理程序论文 关键词：本体；文本分类；特征降维；分类模型 [gap=791]Key words: ontology; text categorization; feature dimension; classifier model
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feature dimension reduction 特征降维
ear feature dimension 人耳特征维数
feature dimension incremental learning 特征维数增量学习
fractal dimension feature 分形维数特征
two dimension structure feature 二维结构特征
space dimension feature 空间尺度特征
feature e dimension 形体尺寸
As feature dimension increases continually,search algorithms costs a lot of time.
随着特征维数的不断增长,搜索性特征选择算法付出巨大的时间代价,而非搜索性算法则由于其时间代价小,而且能有效去除冗余特征等优越性越来越受到关注。
参考来源 - 基于KNN算法的组合式非搜索特征选择算法 in C·2,447,543篇论文数据，部分数据来源于NoteExpress
Based on a detailed study of these processes, this thesis focuses on characteristics of feature dimension reduction and feature weighting.
本文在对这些过程进行详细了解和研究的基础之上，重点探讨了特征降维和特征加权过程。
Actually the kernel design in the recognition method based on discrete time - frequency representation is a problem of feature selection from the ambiguity functions to reduce feature dimension.
基于离散时频分布的信号识别方法，将时频核设计问题转化为以信号自模糊函数为原始特征的特征选择问题，以实现特征降维和信号识别。
First of all, pretreatment of human ear pictures was completed. Then an improved 2dpca algorithm was used to compress feature dimension. Finally, BP neural network was used in ear classification.
该方法首先对人耳图片进行预处理，然后采用改进的两级2dpca算法，进一步压缩提取的人耳特征维数，最后采用BP神经网络进行分类识别。
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