特征向量选择,feature vector selection英语短句,例句大全

特征向量选择,feature vector selection
1)feature vector selection特征向量选择
1.A fast principal component analysis method is proposed to solve the computation problem for kernel matrix K based on a multi-block feature vector selection.针对核主成分分析方法(KPCA)存在大样本集的核矩阵K计算困难问题,提出一种基于分块特征向量选择的快速核主成分分析方法。
2.Then a geometry-based feature vector selection(FVS) scheme was adopted to reduce the computational complexity of KFDA.首先采样数据经过小波变换方法去除噪声,去除噪声后的数据进行KFDA建模,然后在建模同时采用特征向量选择(FVS)算法降低复杂性。
2)FFVS快速特征向量选择
1.In order to deal with it, a fast feature vector select (FFVS) scheme is adopted that divides the total images into several groups according to.为了解决这一问题,提出一种快速特征向量选择法(FFVS)。
3)feature selection特征变量选择
1.The approach is composed of three phases:(1)feature selection is accomplished by the Simba algorithm;(2)the initial fuzzy system is identified using the fuzzy clustering algorithm;(3)the structure and parameters of the fuzzy system are optimized by the Pittsburgh-style real-coded genetic algorithm.该方法由以下三步组成:(1)利用Simba算法进行特征变量选择;(2)采用模糊聚类算法辨识初始的模糊模型;(3)利用匹茨堡型实数编码的多目标遗传算法对初始模糊模型的结构和参数进行学习。
4)feature selection特征选择
1.Fast VQ algorithm for hyperspectral image compression based on feature selection;基于特征选择的高光谱图像快速矢量量化算法
2.A feature selection method based on ReliefF evaluation and complementary coefficient;一种基于ReliefF评估和互补系数的特征选择算法
3.Application of union adaptive algorithm based on PCA and rough set for feature selection in face recognition;PCA和粗糙集的联合自寻优特征选择在人脸识别中的应用

英文短句/例句

1.Study on Feature Selection and Ensemble Learning Based on Feature Selection for High-Dimensional Datasets;高维数据的特征选择及基于特征选择的集成学习研究
2.A Feature Selection Method Based on Class Feature Domains for Text Categorization基于类别特征域的文本分类特征选择方法
3.The Application of Feature Extraction and Selection in Handwritten Digit Recongnition;特征提取和特征选择在手写数字识别中的应用
4.Feature Selection Based on Feature Fuzzify and Neural Networks;基于特征模糊化和神经网络的特征选择
5.Feature Selection Based on Mutual Information Maximization and Feature Clustering基于互信息最大化和特征聚类的特征选择
6.The key operation of data preprocessing is feature selection and extraction.数据预处理的关键是特征选择和提取。
7.AN EUCLIDEAN DISTANCE FEATURE SELECTION METHOD IN PATTERN RECOGNITION模式识别中欧氏距离特征选择新方法
8.Class information feature selection method for text classification文本分类中的类别信息特征选择方法
9.A Research on Feature Selection in Printed Chinese Character Recognition印刷体汉字识别特征选择方法的研究
10.Study on Feature Selection for Music Classification;面向音乐分类的特征选择方法的研究
11.Research on Feature Selection Methods in Information Filtering System;信息过滤系统中特征选择算法的研究
12.The Study on Feature Selection Algorithm in Chinese Text Clustering;中文文本聚类中特征选择算法的研究
13.Research on High-Performance Feature Selection and Text Categorization;高性能特征选择及文本分类算法研究
14.Choose of Character Feature Based on Zernike Moment;基于Zernike矩的字符特征选择
15.Research on Prototype Based Methods for Feature Selection Algorithm基于原型方法的特征选择算法的研究
16.These include character displacement, habitat selection.这些包括特征置换、栖息地的选择。
17.Selection and Analysis on Eigenvector in Detection of Abnormal Sound of Bearings轴承异常声检测的特征量选择及分析
18.High & New Technology Enterprises Project Financing Characteristic and Finance Strategy Choosing;高科技企业项目融资特征及策略选择

相关短句/例句

FFVS快速特征向量选择
1.In order to deal with it, a fast feature vector select (FFVS) scheme is adopted that divides the total images into several groups according to.为了解决这一问题,提出一种快速特征向量选择法(FFVS)。
3)feature selection特征变量选择
1.The approach is composed of three phases:(1)feature selection is accomplished by the Simba algorithm;(2)the initial fuzzy system is identified using the fuzzy clustering algorithm;(3)the structure and parameters of the fuzzy system are optimized by the Pittsburgh-style real-coded genetic algorithm.该方法由以下三步组成:(1)利用Simba算法进行特征变量选择;(2)采用模糊聚类算法辨识初始的模糊模型;(3)利用匹茨堡型实数编码的多目标遗传算法对初始模糊模型的结构和参数进行学习。
4)feature selection特征选择
1.Fast VQ algorithm for hyperspectral image compression based on feature selection;基于特征选择的高光谱图像快速矢量量化算法
2.A feature selection method based on ReliefF evaluation and complementary coefficient;一种基于ReliefF评估和互补系数的特征选择算法
3.Application of union adaptive algorithm based on PCA and rough set for feature selection in face recognition;PCA和粗糙集的联合自寻优特征选择在人脸识别中的应用
5)characteristic selection特征选择
6)feature extraction特征选择
1.After a brief introduction of the theories of Extension Matrix and Rough Set, Greedy Algorithm, and Heuristic search Algorithm, this paper compares their performance in signal feature extraction applications.简单介绍了基于扩张理论和粗集理论的优化算法、“贪心算法”和启发式搜索算法等典型特征选择的优化算法,并比较它们的性能,在此基础上选择启发式搜索算法用于信号特征的选择。
2.Traditional feature extraction method treats each feature separately and disregards the semantic feature such as relativity and comparability.它的关键技术包括网页清洗、中文分词、特征选择、文本表示以及分类算法。