搜索资源列表
PCA
- 针对稀疏表示识别方法需要大量样本训练过完备字典且特征冗余度较高的问题，提出了结合过完备字典学习与PCA降维的小样本语音情感识别算法.该方法首先用PCA降维方法将特征降维，再将处理后的特征用于过完备字典训练与稀疏表示识别方法，从而给出了语音情感特征的稀疏表示方法，并确定了新算法的具体步骤.为验证其有效性，在同等特征维数下，将方法与BP, SVM进行比较，并对比、分析语音情感特征稀疏化前后对语音情感识别率、时间效率以及空间效率的影响.试验结果表明，所提出方法的识别率比SVM与BP高 与采用稀疏化前的
chapter13
- pca,svm人脸识别,有训练，识别，和orl人脸库-pca,svm source code, can detect human faces
ORLPPCAPSVM
- 一个完整的人脸识别算法实验，快速pca+svm算法，里面还带有orl人脸数据库，并且代码还有相应注释，大小有几十m，是一个很好的人脸实验-A complete face recognition algorithm experiments, fast pca+svm algorithm, which also comes with orl face database, and the code as well as the corresponding notes, there are dozens
Neural-Network
- This folder contains the following sub-folders which are essential in our project: 1.Raw Data All the raw data collected from Flagstaff hill, CMU Athletic Field, and Railroad on Neville St. 2.Filter Filter to rule out signal of Channel
svm
- svm工具箱，程序与应用实例 scaleForSVM:归一化 pcaForSVM:pca降为预处理 fasticaForSVM:ica降为预处理 等-svm toolbox, procedures and application examples
SVM
- 支持向量机用于训练和分类，包括PCA降维等函数-Support vector machines for training and classification, including functions such as PCA dimension reduction
face
- Matlab PCA+SVM人脸识别,通过PCA和SVM算法达到人脸识别的功能。-Matlab PCA+SVM，To identify people s face.
PCA-BEL(ToolBox1.0)
- 又提供一基于生理的情感神经网络，包含杏仁体，丘脑，视觉神经等重要元素，可用于分类与预测，性能优越于SVM，BP-The use of neural network based on emotional physiology, contains important elements of the amygdala, thalamus, optic nerve, etc., can be used for classification and prediction, superior perform
SVM
- 支持向量机SVM和核函数的MATLAB程序集,用于图形处理的算法，分类算法-pca and svm use MATLAB
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
Classification-MatLab-Toolbox
- 模式分类工具箱，有PCA、SVM、ID3源代码，用于数据分析、模式识别和机器视觉。-Pattern classification toolbox, there PCA, SVM, ID3 source code for data analysis, pattern recognition and machine vision.
code-faceGUI.m
- 图形界面的人脸识别代码，PCA+SVM多分类，yale图片库-Face recognition codes in GUI, PCA+SVM multi-classification, yale gallery
pca_svm
- PCA+svm算法进行人脸识别，识别率在百分之80~90- Face recognition algorithm Pca+ support vector machine Recognition rate of about ninety percent, interested friends can be used as a reference
SVM--ICA-and-PCA-and-NN
- SVM,ICA,PCA,NN等等模式识别算法,很有参考-SVM, ICA and PCA and NN, and so on pattern recognition algorithm, is of great reference value
FaceRec_SourceCode
- 基于PCA-SVM的人脸识别，平均识别率达83 ，是基于matlab开发的。-PCA-SVM-based face recognition, the average recognition rate of 83 , based on matlab development.
classification_toolbox
- 多种用于分类的matlab代码，包含PCA,SVM,PLS-DA，KNN,SOMF等.-For various categories of matlab code, and contains the PCA and SVM, PLS- DA, KNN, SOMF, etc.
machine-learning-ex2-8
- 斯坦福机器学习网上公开课相关编程练习代码，包括线性回归，逻辑回归，神经网络，PCA,SVM等。-the programming code of online course Mechine Learning provided by Stanford.
PCA_SVM face recognition
- relize the idea of the face recognition of PCA-SVM
face-recognition-of-svm-and-pca
- the face recognition of svm and pca-the introduaction of svm and pca,and the use of them in face recognition.
FaceRec
- 人脸表情识别matlab程序PCA+SVM算法，SVM分类-orL人脸数据库有数据有图片-Facial expression recognition matlab program PCA+SVM algorithm, SVM classification-orL face