搜索资源列表
PCA-SVM
- 在PYTHON里面，采用LIBSVM，实现对TE数据的多类故障的分类。-In PYTHON inside, using LIBSVM, TE data to realize the classification of many types of failures.
stprtool15aug08
- 统计模式识别算法包，包括线性分类算法，SVM，PCA，LDA，EM，k-means分类等多种常用的模式识别算法。-Statistical pattern recognition algorithm package, including a linear classification algorithm, SVM, PCA, LDA, EM, k-means classification and many other commonly used pattern recognition algori
pca
- 主成分分析程序，可用于数据降维及特征提取。-Principal component analysis procedures, can be used for data dimensionality reduction and feature extraction.
PCA
- 对输入的高维特征向量进行pca降维后输出低维的特征向量-PCA dimensionality reduction
pca-svm
- 使用pca和svm方法对表情进行分类，有较高的识别准确率-The use of pca and expression svm classification methods, which have a higher recognition accuracy
pca
- 神经计算的实验作业。用principle components analysis计算模式的主分量。提取线性输入的特征。-Neural computing experiment operations. Computing model using principle components analysis of the principal component
PCA
- Principal component analysis,for study about classification data,develop for svm , lvq etc-Principal component analysis,for study about classification data,develop for svm , lvq etc
023479SVMmatlab
- svm source code in matlab with pca
svm
- SVM分类器 分类各种图片的类别 分类各种图片的类别 -SVM classifiers various pictures of various categories of classification of classified images of various image types
PCA-SVM
- 利用PCA-SVM的图片降维和识别分类，并分析重建误差等的主程序-The use of PCA-SVM pictures dimensionality reduction and identification and classification, and analyze the main reconstruction error, etc.
]ORL+PCA+SVM-11
- 编写了用户界面程序实现ocr人脸数据集的识别，使用了svm分类器(A user interface program is developed to realize the recognition of OCR face data set, and the SVM classifier is used)
PCA-SVM-master
- PCA/SVM算法实现图像分类，分类准确率可到达90%(Image classification by PCA/SVM algorithm)
PCA
- SVM(Support Vector Machine)指的是支持向量机，是常见的一种判别方法。在机器学习领域，是一个有监督的学习模型，通常用来进行模式识别、分类以及回归分析。(SVM (Support Vector Machine) refers to support vector machines, which is a common discriminant method. In the field of machine learning, it is a supervised learni
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)
PCA+SVM
- 采用经典的ORL人脸数据集，利用PCA进行进行降维，然后用SVM进行数据集的分类和训练。上传文件内包含libSVM3.2安装包(The classical ORL face dataset is used for dimension reduction by PCA, and then SVM is used to classify and train the dataset.)
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)
脑电数据PCA处理及SVM分类
- 脑电eeg数据预处理，用于脑电信号的MATLAB处理程序，输入处理数据，进行matlab运算，PCA处理及SVM分类。(PCA Processing and SVM Classification of EEG Data)
基于PCA的SVM分类
- 选择“BreastCancer”数据集，使用支持向量机(SVM)对其进行分类。作为对比，第一次对特征集直接进行支持向量机分类，第二次对特征集进行主成分分析法的特征提取后，再对特征提取后的特征集进行支持向量机分类。并且对比和分析了两次分类的结果。(The BreastCancer data set is selected and classified by Support Vector Machine (SVM). For comparison, the first time the featur
PCA+SVM的人脸识别
- 使用pca和svm的方法对人脸进行识别和检测，最终达到人脸识别的功能(Face recognition and detection using PCA and SVM methods, and finally achieve the function of face recognition)
基于PCA和SVM的人脸识别系统
- 先通过图像处理提取人脸的各个特征，然后对人脸通过PCA进行降维，然后通过SVM进行人脸识别(Firstly, the features of human face are extracted by image processing, then the dimension of human face is reduced by PCA, and then the face is recognized by SVM)