搜索资源列表
haykin_nn
- Simon Haykin的 《Neural NetWorks》例子原码，相当经典。相信很有用，特别SVM PCA等-Simon Haykin "Neural NetWorks" examples of the original code, very classic. I believe very useful, especially in such SVM PCA
svm
- 支持向量机(SVM)、线形鉴别分析(LDA)、主分量分析(PCA)和人工神经元网络(ANN)源代码
pca-svm
- 本程序用于对训练样本提取独立主元，作为样本特征，并送入SVM分类器中训练图像的预处理中不取对数，也无须做幅度归一，由ICA的应用条件决定的。预处理后的图像以向量的形式按行排列
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
- 基于pca的人脸识别程序，人脸库需要自己下载，供参考-Pca-based face recognition program needs to download face database, for reference
face-gabor-pca
- 基于gabor的人脸识别 带有pca降维 最后用svm识别-Finally, svm pca dimensionality reduction recognition with face recognition based on gabor
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)
SVM-KMExample
- examples of SVM, PCA , MultiSVM, Feature extraction, kernel function
]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
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)
python_face_recog
- 基于python+opencv 的 人脸识别，对一段视频进行读取，并检测出人脸，然后进行PCA 降维，最后用SVM进行人脸识别，识别率94%左右。(Based on python + opencv face recognition, a video was read, and face detection, and then PCA dimension reduction, and finally SVM face recognition, recognition rate of about 9
PCA+SVM
- 用于人脸识别，包含了PCA及SVM算法，数据集采用的ORL数据库(face recognition(PCA+SVM))
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+SVM
- 先用PCA降维，在利用支持向量机进行分类，这个分类是二分类，所以PCA的降维降到两维即可分类。(Firstly, PCA dimensionality reduction is used to conduct classification with support vector machine. This classification is binary classification, so the dimensionality reduction of PCA can be reduced t
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)