1) linear discriminant analysis(LDA)
线性鉴别分析(LDA)
2) Linear Discriminant Analysis(LDA)
线性判别分析(LDA)
3) LDA
线性判别分析(LDA)
1.
In this work, we developed a new approach by combining a new feature representation involving scores of generalized base properties combined with auto cross covariance (ACC), linear discriminant analysis (LDA) and support vector machines (SVM) to predict chromatographic retention time of DNA, and to identify human miRNAs, vertebrate promoters , human exons.
本文通过一种新的途径,即以广义碱基性质得分(SGBP)结合自交叉协方差(ACC),线性判别分析(LDA)和支持向量机(SVM)建模,进行了DNA色谱保留指数、人类miRNA、脊椎动物启动子和人类蛋白质编码基因预测或识别,采用自检验、交互验证、外部验证等方法验证模型的预测能力。
2.
(3)This paper introduced theory of LDA algorithm and its application to face recognition and acquired a great many experiments.
(3)介绍了线性判别分析(LDA)人脸识别方法并且得到一些有意义的结论;并且对该方法进行了改进。
4) LDA
线性判决分析(LDA)
5) linear discriminant analysis
线性鉴别分析
1.
Fisher linear discriminant analysis(LDA) and Maximum Scatter Difference Discriminate Analysis(MSDDA) are firstly adopted to extract two sets of features in the same pattern space,respectivel.
为了有效地融合Fisher线性鉴别分析与最大散度差鉴别分析所抽取的特征,得到更加全面反映原始样本的鉴别特征集,提出了基于典型相关分析的增强线性鉴别分析方法。
2.
Based on linear discriminant analysis a arithmetic was proposed.
基于线性鉴别分析原理,给出了一个拟合度判断算法。
3.
Uncorrelated discriminant analysis is a very effective method for linear discriminant analysis and plays an important role in discriminant analysis.
不相关鉴别分析是一种非常有效并起着重要作用的线性鉴别分析方法,它能抽取出具有不相关性质的特征分量。
6) linear discriminant analysis(LDA)
线性鉴别分析
1.
Direct LDA(DLDA) is an extension of Linear Discriminant Analysis(LDA) to deal with the small sample size problem,which is previously claimed to take advantage of all the information,both within and outside of the within-class scatter\'s null space.
直接线性鉴别分析(DLDA)是一种以克服小样本问题而提出的LDA扩展方法,被声明利用了包含类内散布矩阵零空间外的所有信息。
补充资料：多元线性回归分析
在线性相关条件下，研究两个或两个以上自变量对一个因变量的数量变化关系，称为多元线性回归分析。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条