Through support vector machine algorithms for gene expression data classification training, SVMs provide a effective way for analysis of gene expression data.
通过支持向量机训练算法对基因表达数据进行分类训练，为分析基因数据提供有效的手段。
Objective Discuss the condition and the effect of SVM in the classification of gene expression data.
目的探讨支持向量机在基因表达数据分类研究中的应用条件和效果。
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.
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。
The gene linear profile model, composed of model profiles and coefficients, is obtained by ica from gene expression data, so gene classification based on ica is presented.
利用ICA对基因微阵列表达谱数据进行分解获得由基因模型谱和对应系数构成的线性谱模型，并在此基础上进行基因分类。
The problem of feature gene selection and tumor samples classification of microarray gene expression data is one of challenges of gene microarray technology.
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。
This thesis improves classification using gene expression data method in two aspects: feature selection and SVMs classification algorithm.
针对基于基因表达数据的分类，本文从特征基因选择和支持向量机分类算法两个方面进行了改进。
Several classification methods based on DNA gene expression microarray data are introduced in this paper.
介绍了目前几种基于DNA微阵列基因表达数据的分类方法。
This thesis improves tumor samples classification of gene expression data in two aspects: classification algorithm and feature gene selection 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.
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。
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