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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.)
mfcc_svm
- mfcc特征提取法 以及svm训练 可以使用(MFCC feature extraction method and SVM training can be used)
classifier_D
- 使用SVM分类器来预测乳腺癌病人的预后(特征选择；分类器构建)，评价模型时使用无被交叉验证，性能评价指标包括准确率，AUC，灵敏度，特异度。学会最基本的机器学习方法。可查看分发给大家的代码，以后遇到类似的问题，可用相似的思路和代码。(The SVM classifier was used to predict the prognosis of breast cancer patients (feature selection; classifier construction), and the
BPSO
- 二元粒子群优化（BPSO）用于特征选择任务，可以选择潜在特征，提高分类精度。(binary particle swarm optimization (BPSO) for feature selection tasks, which can select the potential features to improve the classification accuracy.)
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
基于粒子群优化算法的特征选择SVM分类
- 针对“BreastCancer”数据集，作为对比，第一次对特征集直接进行SVM分类，第二次使用粒子群算法进行特征选择后再进行SVM分类。并且对比和分析了两次分类的结果。(For "BreastCancer" data set, as a comparison, the first time the feature set is directly classified by SVM, and the second time the feature set is selected
cntData_CSP_FLDA
- 本算法针对运动想象的脑电数据，进行预处理并后续用分类器做分类。 该实验所用的的脑电特征提取方法主要是csp空间滤波，并后续用FLDA来进行特征分类。最终得到较好的效果(In this algorithm, the EEG data of motion imagination are preprocessed and then classified by classifier. The main feature extraction method of EEG in this experime
19854815template-matching_CSDN
- 对图像通过提取相应特征，来进行分类，效果很好(Classify images based on features, which works well)
多重分类算法
- 基于矩阵特征空间分解的方法。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。
汪星人识别项目
- python语言，使用keras框架，用vgg16提取图片特征然后用全连接层分类(Python language, using keras framework, extracting image features with vgg16 and classifying with full connection layer)