搜索资源列表
adaboost-and-rbf
- 随机森林算法在图像特征分类回归中的应用，通过结合神经网络进行更好的特征数据处理-Application of random forest algorithm in image classification and regression, better features by combining neural networks data processing
1forest
- 森林算法实现，主要实现多特征分类实现，代码通过实际测试，可以实现分类任务-forest algorithm can be used to implement multi-features classification task
face
- 基于opencv自带的Haar特征分类器的人脸识别，可识别完整的人脸并用圆圈圈出。-Based opencv comes Haar classifier recognition feature, you can identify the full face and a circled.
Project_face_detect
- 使用opencv自带的haar特征分类器，进行人脸检测的程序代码。-A program with opencv inside classifier for face detection
1
- 主要是python环境下用来实现lr逻辑回归模型特征分类的源码-The main source is used to achieve lr logistic regression models feature classification under python environment
facedetect-test
- 基于hog特征与支持向量机的方式，使用opencv自带的haar特征分类器，检测人脸-Adapt to hog feature and support vector machine (SVM) method, based on positive and negative samples opencv training face,
BBO-MLP
- 生物地理算法优化多层感知网络进行特征分类和识别，精度高达97 -Bio geographic algorithm to optimize multi layer perceptron network for feature classification and recognition, the accuracy of up to 97
km
- KM算法，通过对图片的像素点特征分类，实现图片杂质检测等功能-KM algorithm, through the image of the pixel classification to achieve image impurity detection and other functions
55711236
- 用MATLAB编写的svm源程序,可以实现支持向量机,用于特征分类或提取,-MATLAB prepared by the SVM source program, can realize the support vector machine (SVM), used for extracting feature classification or,
amspmbly
- 用MATLAB编写的svm源程序,可以实现支持向量机,用于特征分类或提取,-MATLAB prepared by the SVM source program, can realize the support vector machine (SVM), used for extracting feature classification or,
模式识别第一次作业
- 1． 用 dataset1.txt 作为训练样本，用dataset2.txt 作为测试样本，采用身高和体重数据为特征，在正态分布假设下估计概率密度（只用训练样本），建立最小错误率贝叶斯分类器，写出所用的密度估计方法和得到的决策规则，将该分类器分别应用到训练集和测试集，考察训练错误率和测试错误率。将分类器应用到dataset3 上，考察测试错误率的情况。(1. using dataset1.txt as training samples as test samples by dataset2.tx
PCA_gabor_svm
- Gabor小波变换和PCA降维在用SVM分类(Gabor wavelet transform and PCA dimension reduction are classified in SVM)
Svm
- SVM实现图片分类，SIFT特征，可以自动读取文件下的图片和目录名(SVM realize picture classification, SIFT features, you can automatically read files under the pictures and directory names)
SVMRFE.m
- 基于RFE特征选择方法的多分类特征排序，Matlab平台(Multi class feature ranking based on RFE method)
hog_svm
- 利用HOG算子提取特征，利用支持向量机进行分类，得到了较好的图像分割效果(Using HOG operator to extract features, using support vector machine to classify, get a better image segmentation effect)
LBP_Matlabcode
- 提取头像的lbp特征，用于提取图像中比较明显的部分，用于分类(used to extract the obvious parts of the image for classification)
chapter1
- 语音特征信号分类，利用神经网络进行语音特征信号分类(Voice feature signal classification, the use of neural networks for voice feature signal classification)
XQDA
- 用于度量学习，可以用于特征分类，最常应用于行人重识别的研究过程(For metric learning, it can be used in feature classification, and is most often used in the research process of pedestrian re identification)
sigma点的代码
- 基于分割的局部Sigma语义特征点，是对场景中的语义目标进行建模。先在传统的图像分割基础上，分割出场景的前景目标，再结合像素位置、颜色、Gabor特征和LBP特征[构造出表征目标语义信息的协方差描述子，最后将其转换成欧式空间下的Sigma点特征，适用于标准SVM的场景学习和分类。(The segmentation based local Sigma semantic feature points are modeling the semantic objects in the scene. In
案例1 BP神经网络的数据分类-语音特征信号分类
- 通过BP算法，实现对语音特征信号的数据分类(Through the BP algorithm, the realization of the classification of speech signals)