实验结果表明：我们的特征抽取和识别方法非常有效，由此设计的分类器具有较高的识别率。
Experimental results have shown that our feature extraction and recognition method is very effective, and the designed classifier has high recognition performance.
人脸识别一般分为人脸检测、特征抽取和模式分类三个部分。
Face recognition normally be regarded as have three processes that are face detection, features extraction and pattern classification.
给出一种抽取纹理特征的算法，该算法实时性强，适于在线遥感图像分类。
This paper proposes a fast algorithm for texture feature extraction. The new algorithm is suitable for remote image classification on line.
这方面的工作涉及了音乐旋律的表达、旋律特征的抽取以及分类技术等许多内容。
This work includes many questions, such as melody representation, melody character extraction and classifying technology.
通过样本采集、特征抽取、支持向量机训练得到最终的人体目标识别分类器。
Human objects recognition classifier is obtained through samples collecting, feature abstract and SVM training.
首先自动从评论中识别用户评价的商品特征，根据特征对评论句分类，然后使用句子抽取的方法生成摘要。
It first mines product features from reviews, then classifies reviews at the granularity level of sentence, and at last generates summary via sentence extraction.
本文将二者结合起来，用小波变换抽取特征、用自适应共振art网络作模式分类器来识别手写数字。
This paper combines the two aspects to recognize handwritten digits by using wavelet transform to extract feature and Adaptive Resonance Theory (ART) Neural Networks for Classification.
只要预处理、特征抽取及分类得当，时间间隔和实验误差等因素对运动想象识别的影响不大。
With the suitable pretreatment, features extraction and classifier design, the influence, which arose from time interval, experiment error and so on, can be nearly ignored.
基于规则的主要思路是通过分类文本的特征、结构等信息，寻找到一些用于抽取的规则。
The main idea to rules-based model use text documents of the characteristics, structure and so on, to find some rules for extraction.
在此提出一种基于类别核心词的概念映射方法，首先从文本中抽取类别核心词，借助《知网》将特征词映射到基于类别核心词的概念空间，然后在概念空间上完成文本分类工作。
The idea is to extract the core words of class first, then use HowNet to map key words space to concept space based on core words, finally finish the text classification pr.
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.
为了提高分类正确率和减少训练时间，将特征抽取技术与分类算法结合，提出了一种基于KFDA - SVM的入侵检测技术。
In order to improve the detection rate and reduce the training time, KFDA-SVM intrusion detection technology is proposed which combines the feature extraction technology and classification algorithm.
该部分通过抽取网页的特征项，形成文本向量，然后与中心向量进行相似度计算后，根据相似度的结果来对网页进行自动分类。
After calculate the text vector and main vector, the system will judge which kind the page is by the result of calculation.
该部分通过抽取网页的特征项，形成文本向量，然后与中心向量进行相似度计算后，根据相似度的结果来对网页进行自动分类。
After calculate the text vector and main vector, the system will judge which kind the page is by the result of calculation.
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