搜索资源列表
pca
- PCA主元分析后用神经网络预测，A/S含量，PCA算法实现，与神经网络-PCA principal component analysis using neural network prediction, A/S content, PCA algorithm, and neural network
pca(ica)svm
- 基 于 pca(ica)-svm 实 现 故 障 诊 断 -PCA_test.......
knnPcaWithGA
- In this code I have used GA in supervised PCA to find the best coeficients for overall covariance. the classification is made by K-In this code I have used GA in supervised PCA to find the best coeficients for overall covariance. the classification i
faceRecognition
- 基于SVM和PCA的人脸识别，使用了ORL人脸数据集和libsvm.jar-Face recognition based on SVM and PCA. ORL faces dataset and libsvm.jar are used
PCAPSVMPRF
- PCA测试程序，结合SVM及随机森林进行分类，测试PCA降维后对分类精度的影响。-PCA test ,SVM_RandomForest
Classification-MatLab-Toolbox
- matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值--attern recognition Matlab toolbox, including SVM, ICA, PCA, NN pattern recognition algorithms, and so on, of great reference value
fanheng
- 可以提取一幅图中想要的目标，包括最小二乘法、SVM、神经网络、1_k近邻法,结合PCA的尺度不变特征变换(SIFT)算法。- Target can be extracted in a picture you want, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Combined with PCA scale invariant feature transform (SIF
suisang_v27
- 是学习PCA特征提取的很好的学习资料，Pisarenko谐波分解算法,包括最小二乘法、SVM、神经网络、1_k近邻法。- Is a good learning materials to learn PCA feature extraction, Pisarenko harmonic decomposition algorithm, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
PCA-SVM
- 用于主成分图像svm分类，简单，有很好的程序，适合初学者(SVM for principal component image classification, simple, there are very good procedures for beginners)
rzcrgnition
- 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 and PCA and NN, and so on pattern recognition algorithm, is of great reference value)
face-SVM
- 用PCA和SVM实现人脸识别，是经典的人脸识别Python代码(Face recognition using PCA and SVM)
SVM_Mdl.mat
- These files are matlab source code for price forecasting for smart meter hourly data. Step 1 relevant features are selected by Gray Correlation, Random Forest, Relief F algorithms. Then Kernel PCA of features are calculated. Price is predicted by Ker