1.
Kernel principal composition analysis of sensory and physiochemical quality of fermented bean curd
腐乳感官和理化品质的核主成分分析
2.
KPCA extracting principal component with nonlinear method is an improved conventional PCA.
核主成分分析方法是主成分分析的改进算法，其采用非线性方法提取主成分。
3.
Application of the Kernel Principal Component Analysis in Comprehensive Evaluation of Enterprise Economic Results;
核主成分分析(KPCA)在企业经济效益评价中的应用
4.
A Weighted Kernel PCA and the Related Parameters Choice
一种加权核主成分分析及其相关参数的选取
5.
Image feature extraction from wavelet scalogram based on kernel principle component analysis
基于核主成分分析的小波尺度谱图像特征提取
6.
So KPCA can extract the feature set more suitable in categorization than classical conventional PCA.
核主成分分析与传统主成分分析相比，可以得到更好的适合分类的特征。
7.
Research on Text Categorization Based on Kernel PCA and RBF Neural Network
基于核主成分分析和径向基神经网络的文本分类研究
8.
Edge Detection Method Based on Kernel Principal Component Analysis and Subspace Classification
基于核主成分分析和子空间分类的边缘检测方法
9.
Research on Intrusion Detection Based on C-SVM and KPCA;
基于软间隔支持向量机和核主成分分析的入侵检测研究
10.
An Application Research on Face Recognition Based on KPCA Feature Extract and SVM;
基于核主成分分析特征提取及支持向量机的人脸识别应用研究
11.
Application of the Kernel Principal Component Analysis Method to University S&T Innovation Capability Evaluation;
基于核主成分分析的高校科技创新能力评价研究
12.
HYPERSPECTRAL REMOTE SENSING IMAGE FEATURE EXTRACTION BASED ON FUZZY KERNEL PRINCIPAL COMPONENT ANALYSIS
基于模糊核主成分分析的高光谱遥感影像特征提取研究
13.
Application of the Kernel Principal Component Analysis in Comprehensive Evaluation of Farm Machinery Performance
核主成分分析方法在农业机械性能综合评价中的应用
14.
Efficient Kernel Principal Component Analysis Algorithm for Large-Scale Data Set
一种解决大规模数据集问题的核主成分分析算法
15.
SAR Images Recognition Based on Modified Kernel Principal Component Analysis
一种基于改进核主成分分析的SAR图像识别方法研究
16.
LS-SVM Regression Based on KPCA
基于核主成分KPCA的LS-SVM的算法分析
17.
Principal component analysis and correlation analysis on Chinese peanut core collection
中国花生核心种质的主成分分析及相关分析
18.
The Application of Principal Components Analysis to the Evaluation of Students Affairs;
主成分分析法在学生工作量化考核中的应用