搜索资源列表
Classification-MatLab-Toolbox
- 模式识别matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值-pattern recognition Matlab toolbox, including SVM, ICA, PCA, NN pattern recognition algorithms, and so on, of great reference value
statistic_pattern_reconnition_toolbox
- 模式识别中统计模式识别的方法,包括贝叶斯，统计学习，LDA，PCA，SVM的经典方法，是不可多得的算法的toolbox-pattern recognition statistical pattern recognition methods, including Bayesian statistics study, LDA, PCA, SVM classical method, the algorithm is rare in toolbox
biaoqing
- 一个表情识别的源码。直接调用pca和svm函数。-an expression recognition of the source. Pca and direct calling svm function.
Yale_PCASVM
- 在Yale 人脸库上运用PCA＋SVM的方法实现了人脸检测，并统计识别率
facedetector
- 人脸检测源代码. The souce demonstrates face detection SSE optimized C++ library for color and gray scale data with skin detection, motion estimation for faster processing, small sized SVM and NN rough face prefiltering, PCA/LDA/ICA/any dimensionality reduct
1087
- pca+svm源代码（matlab） matlab代码，pca进行特征提取，svm进行分类
边缘检测算法
- 该工具箱专为模式识别定制，主要是数字图像识别，比如特征提取、图像分类、PCA、LDA、ICA、DCT、RBF、RBE、GRNN、KNN、minimum distance、SVM等等
PCAPSVM
- PCA+SVM，对图像进行降维分类，并在yale库上测试取得比较好的效果-PCA+ SVM, dimensionality reduction of image classification, and yale library to achieve better test results
DIPDemo
- 《数字图像处理与机器视觉:Visual C++与Matlab实现》7 V图像的点运算,几何变换, 图像增强,彩色图像处理,实用案例——汽车牌照的投影失真校正-" Digital image processing and machine vision: Visual C++ and Matlab to achieve" 6 support vector machines, comprehensive case- based on PCA and SVM for Face Re
PCA_ORL
- Matlab环境下，实现用PCA方法提取EigenFace,之后通过SVM方法对人脸图像进行分类识别。-Face recognition via PCA and SVM method
4
- 本程序用pca，kpca,svm,pls,fisher实现cstr和csth过程的故障检诊断，检测率为百分之九十九，故障识别率为百分之九十六-The program use pca, kpca, SVM, PLS, fisher realize CSTR process inspection and CSTH fault diagnosis, detection rate was ninety-nine percent, the fault recognition rate is ninety-
Attribute profiles
- 选择合适的样本特征点，然后可以将特征导入svm进行分类(After the image processing, the main information is obtained by PCA transform, and then the feature of texture information selection is put forward)
基于主分量的人脸重构
- 本实验是基于主成分分析法（PCA）在人脸识别中的应用，采用SVM分类器在ORL人脸库的基础上通过Matlab实现了快速PCA算法的验证仿真。
face-Adaboost
- 用Adaboost和PCA算法实现人脸识别，用Python写的代码，根据经典的PCA和SVM算法改编(Adaboost and PCA algorithm for face recognition, code written in Python, adapted from the classic PCA and SVM algorithm)
PCA-SVM
- 利用主成份分析 SVM 实现 人脸识别(Using principal component analysis SVM to realize face recognition)
matlab
- 用于脑电信号分析的matlab算法，对数据进行PCA处理及SVM分类。(The matlab algorithm for EEG signal analysis performs PCA processing and SVM classification on data.)
贝叶斯人脸识别
- Pattern-Recognition-and-Machine-Learning-master,项目包括使用贝叶斯分类器的字符识别，基于GMM的图像分割，使用PCA的人脸识别和具有径向基函数的多类SVM分类器(Pattern-Recognition-and-Machine-Learning-master)
ML_SVM-master
- 算法功能是SVM分类，使用PCA降维处理，一个文件是直接分类，另一个是降维后分类(Classification using SVM algorithm)
pca
- 做降维处理，做分类，非常好的数据集合，可以用于一般的数据清晰(Decomposition is a very interesting great name and it is very very very good , so you will use it)
PCA-SVM-face
- 使用MATLAB语言，基于主成分分析和支持向量机进行人脸识别(MATLAB face detection)