搜索资源列表
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)
GMM
- 用java实现混合高斯模型，做特征分类，模式识别等应用(The hybrid Gauss model is implemented by Java, and the feature classification and pattern recognition are performed)
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)
33754289
- 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,)
基于图像的道路裂缝识别算法的研究
- 对于四种未修补裂缝分类问题，研究它们在方向以及分布密度上的差异性来进行裂缝类型 的划分。(For the problem of unrepaired crack classification, we use the differences of them on the crack direction and the distribution for crack classification. Using 2D feature mapping, Delaunay t
2
- 基于SVM算法和纹理特征提取的遥感图像分类(based on the SVM algorithm and texture feature extraction of remote sensing image classification)
3
- 基于底层特征和SVM的图像分类image classification based on the underlying characteristics and the SVM(image classification based on the underlying characteristics and the SVM)
C4_5
- C4.5算法，优秀的决策树算法，由于求解特征分类问题(C4.5 algorithm, an excellent decision tree algorithm, especially for the problem of feature classification)
案例1 BP神经网络的数据分类-语音特征信号分类
- 使用BP神经网络的数据分类-语音特征信号分类(Data classification using BP neural networks -- speech feature signal classification)
HOGFeature
- Hog特征提取，提取大量特征用于图像的分类识别(Hog Feature Extraction)
案例1 BP神经网络的数据分类-语音特征信号分类
- MATLAB神经网络案例分析 BP神经网络的数据分类-语音特征信号分类(MATLAB BP Neural Network - Classification of Speech Characteristic Signals)
finallyliuyuClassifier
- 用于文本分类，文本挖掘，文本特征提取，文本聚类，文本关联等(It is used for text classification, text mining, text feature extraction, text clustering, text association, etc.)