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latin_hs
- 实用的抽样方法，适用于总体量大、差异程度较大的情况。先将总体单位按其差异程度或某一特征分类、分层，然后在各类或每层中再随机抽取样本单位。分层抽样实际上是科学分组、或分类与随机原则的结合。分层抽样有等比抽样和不等比抽样之分，当总数各类差别过大时，可采用不等比抽样。(Latin Hypercube Sampling)
SVM
- 使用HOG提取特征，SVM进行图像分类，可以进行两种以上分类(Using HOG to extract features and SVM for image classification)
发电机声音检测与故障诊断研究
- 发电机故障检测与诊断方面的论文。包括发电机声音信号采集、处理，特征值提取和基于BP神经网络的分类。(paper.About:fault detection ,feature extraction.Based on the generator sound detection and fault diagnosis research)
CNN
- 使用cnn提取图像特征，然后用SVM分类，此处没有给出训练集，另外imagenet-caffe-alex部分代码需要注意，需要下载的话把注释掉的代码打开(Using CNN image feature extraction, and then use the SVM classification, there were no given training set, also need to pay attention to imagenet-caffe-alex part of the code
HOG_LBP
- 融合hog与lbp特征的图像分类，使用svm进行分类，最终给出运行混淆矩阵(The image classification of hog and LBP features is classified by SVM, and the run obfuscation matrix is finally given.)
hog_svm_facedet
- 使用hog特征以及svm分类实现人脸检测，准确率高(face detection used hog+svm)
40746336sift-mlab
- 检测并提取图像的SIFT特征，用于图像识别和分类(Identify and extract the SIFT feature points in the image for image recognition and classification)
LBP
- LBP方法（Local binary patterns）是一个计算机视觉中用于图像特征分类的一个方法。LBP方法在1994年首先由T. Ojala, M.Pietik?inen, 和 D. Harwood 提出[43][44]，用于纹理特征提取。(The LBP method (Local binary patterns) is a method for classification of image features in a computer vision. The LBP method f
随机森林分类器
- 对提取好的n维特征，实现随机森林分类器分类。(For the extraction of good characteristics, the realization of random forest classification)
get feature
- 在机器学习图像分类的过程中，对ROI进行形状和纹理特征提取(exact the shape and texture features of ROI)
iris12
- 基于LSSVM的分类器，用于iris的三种分类，4种特征进行3类分类，准确率90%以上(LSSVM FOR IRIS,The classifier based on LSSVM is used for three classifications of iris, and 4 features are classified by 3 categories. The accuracy rate is over 90%.)
deep-learning-HAR-master
- 一份用tensorflow平台做的cnn分类时序信号，是分类UCI 项目中的人体活动识别（HAR）数据集。该数据集包含原始的时序数据和经预处理的数据（包含 561 个特征）(A CNN classification timing signal made by tensorflow platform is a human activity recognition (HAR) dataset in the classified UCI project. The dataset contains or
案例1 BP神经网络的数据分类-语音特征信号分类
- BP神经网络的数据分类-语音特征信号分类(Data classification of BP neural network speech feature signal classification.)
规则图像特征提取
- 运用matlab规则图像特征提取分析，分类(Feature extraction of regular image)
Canupo
- C++编码，基于线性判别分析（LDA）和支持向量机（SVM）的多尺度维度特征点云分类算法，通过机器学习方法精确分类。效果可达95%以上，本文件夹内含有详细中文教程。
spr-7
- 采用两线程随机离散卷积神经网络针对触觉序列进行特征提取与分类。(Two thread random discrete convolution neural network is applied to feature extraction and classification for haptic sequences.)
event-related-desynchronization-master
- 内含BCI2008数据集处理方法，特征提取和机器学习算法，得到了很好的分类效果(Contains BCI2008 data set processing methods, feature extraction and machine learning algorithms, with good classification results)
xiaoboshang_SVM
- 脑机接口分类，特征提取用小波包熵，分类使用SVM分类器(Brain-computer interface classification, feature extraction using wavelet packet entropy, classification using SVM classifier)
基于SOM的数据分类
- SOM神经网络也属于自组织型学习网络，只不过更特殊一点它属于自组织特征的映射网络。该网络是由一个全连接的神经元阵列组成的无教师，自组织，自学习的网络。(SOM neural network also belongs to self-organizing learning network, but more specifically, it belongs to self-organizing feature mapping network. The network is a non-teache
CNN
- 通过卷积网络，自动实现对图片特征的提取，通过训练，得到有效的权值，进行图像分类(Through convolution network, automatic extraction of image features can be realized. Through training, effective weights can be obtained and image classification can be carried out.)