搜索资源列表
rfuncs
- 用随机森林的方法进行特征选择，对200了影像特征数据进行分类(Feature selection using random forest methods)
deepsae
- 构建深度sae网络，数据特征提取及分类，自定义网络结构参数(about deepsae code and an example, ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN)
CSP
- CSP特征提取算法，可用于两类特征的数据分类中(CSP feature extraction algorithm)
test2-BP
- 采用BP神经网络设计男女生分类器。采用的特征包括身高、体重、是否喜欢数学、是否喜欢文学、是否喜欢运动共五个特征，BP神经网络包含一个隐层，隐层结点数为5。(Using BP neural network to design a classifier for male and female students. The features include height, weight, whether they like mathematics, whether they like literatur
libsvm-3.17
- 支持向量机，用于模式识别的特征分类，基于MATLAB的工具包(Support vector machines are used for feature classification of pattern recognition, based on MATLAB Toolkit)
SVM
- 通过HOG获取特征，用SVM对图像进行分类。(The feature is acquired by HOG, and the image is classified by SVM.)
cplst
- 多标签分类算法，通过对标签降维（SVD），然后利用线性回归建立特征和低维标签之间的关系，求出特征的系数，然后反过来进行预测(Multi label classification algorithm, through the tag dimension reduction (SVD), and then use linear regression to establish the relationship between features and low dimensional tags, to
svmtrain
- 基于支持向量机的对指定多个包含特征的训练集图片，包含label信息。训练后，可对于相同格式的图片进行分类。(A training set image containing multiple features is included in the support vector machine (SVM), which contains label information. After training, the pictures in the same format can be classifi
案例1
- BP神经网络的数据分配，对语言特征信号进行分类。(The data distribution of the BP neural network is used to classify the language characteristic signals.)
FeatureExtractionUsingAlexNetExample
- 本示例展示了怎样从一个预处理的卷积神经网络中提取特征，并用这些特征去训练一个图像分类器。(This example shows how to extract learned features from a pretrained convolutional neural network, and use those features to train an image classifier. Feature extraction is the easiest and fastest way use
audio_java
- python提取的乐器MFCC特征，调用TensorFlow 接口预测音频类别(Python extraction of the musical instrument MFCC features, calling the TensorFlow interface to predict the audio category)
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)