在模式识别过程中，通过欧式距离将样本分类。
In the process of pattern recognition, we use Euclidean distance formula to classify sample.
采用本发明，可提高数据样本分类处理的效率。
The invention can improve the efficiency of classified treatment of a data sample.
给出了空间中的样本分类判别准则和分类模型。
The classification criteria and classification model in the Rd space were presented.
结果：利用聚类分析对18个样本分类结果准确。
Results: Precise classification of 18 samples was done by cluster analysis and mahalanobis distance analysis.
利用基于马氏距离的数据分类技术，对输入样本分类。
By adopting data sorting technology based on Mars distance the input samples are sorted.
给出的样本分类方法对各种领域多元参数回归应用具有重要意义。
The sample classification method provided is adaptable to multivariate parameters regression analysis of various fields.
另外，本文还通过实证对支持向量机的小样本分类性能进行说明。
More over, Support Vector Machine is also proposed for classification of small samples.
以模糊区为中心将样本分类，不同类样本与相应的BP子网相对应。
Taking the fuzzy region as the center, we classify the samples into different regions.
然后由基于数字规范化模板特征的分类器对前一级分类器的拒识样本分类。
Then, the classifier based on the normalized template feature is applied to the samples rejected by the former.
基因表达数据的特征基因选取和肿瘤样本分类问题是基因微阵列技术的挑战性课题之一。
The problem of feature gene selection and tumor samples classification of microarray gene expression data is one of challenges of gene microarray technology.
利用所提出的特征，采用适合小样本分类问题的支持向量机(SVM)对足球视频镜头分类。
Support vector machines (SVMs) which suit to classification problem for tiny samples is designed for different shot types through the features extracted by the method.
针对基于基因表达数据的肿瘤样本分类，本文从分类算法和特征基因选取方法两个方面进行了改进。
This thesis improves tumor samples classification of gene expression data in two aspects: classification algorithm and feature gene selection method.
结果表明该分类器在标准文本样本集合上的性能好于其他三种分类器，在小样本分类上具有一定优势。
The results show that it has better performance than the other three classifier on the standard text sample set, and it has some superiority on small set of samples.
结果表明该分类器在标准文本样本集合上的性能好于其他三种分类器，在小样本分类上具有一定优势。
The results show that it has better performance than the other three classifier on the standard text sample set, and it has some superiority...
第三，针对支持向量机算法复杂度较高，难以应用于大样本分类的问题，提出了GMP-CSVC算法。
Thirdly, a GPU based massively data parallel C-SVM classification (GMP-CSVC) algorithm is presented to reduce the training time of SVM.
将SOFM神经网络与医师诊断相结合对腹部MRI进行样本分类，并利用判别分析方法对肝硬化进行图像识别。
The sample classification of abdomen MRI is carried out by the combining method of SOFM Neural network and doctors diagnosis.
在这个研究领域里，基于基因表达数据的样本分类扮演着很重要的角色，它一般具有两个关键步骤：基因选择和分类模型设计。
In these research areas, sample classification based on gene expression data is acting a very important role, it generally has two pivotal steps: gene selection and Classifier design.
论文针对KNN这种常用的文本分类方法，分析了什么是它的典型样本，提出了一种基于密度的样本选择算法。
In the paper, what is the typical sample of KNN is analyzed, and a method of samples selection based density is presented.
组合分类结果受训练样本分布的影响取决于子分类器的稳定性。
It depends on the stability of sub-classification that whether the results of combined classification are affected by the distribution of training samples.
第二、建立文本分类模型，使用大量的有害信息样本数据训练分类模型。
Secondly, Building text categorization model, and training the model by a great many harmful information samples data.
当子分类器均受训练样本分布影响较小，组合结果也具有较好的稳定性。
If the distribution of training samples only had little influence on the sub-classification, the combined classifiers would have stable performances.
而且使用基于本体的短文本分类方法，无须训练样本，可以通过本体获得语义信息并结合相似性计算来实现对短文本的自动分类。
Without training samples when using this method, we can get semantic information of ontology and combine the similarity calculations to achieve the short-text classification.
传统KNN方法的明显缺陷是样本相似度的计算量很大，使其在具有大量高维样本的Web文本分类中缺乏实用性。
The traditional KNN has a fatal defect that time of similarity computing is huge. The practicality will be lost when the KNN is applied to WEB text categorization with high dimension and huge samples.
传统KNN方法的明显缺陷是样本相似度的计算量很大，使其在具有大量高维样本的Web文本分类中缺乏实用性。
The traditional KNN has a fatal defect that time of similarity computing is huge. The practicality will be lost when the KNN is applied to WEB text categorization with high dimension and huge samples.
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