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Subpattern-based_principal___component_analysis.zi
- 子模式主成分分析首先对原始图像分块,然后对相同位置的子图像分别建立子图像集,在每一个子图像集内使用PCA方法提取特征,建立子空间。对待识别图像,经相同分块后,分别将子图像向对应的子空间投影,提取特征。最后根据最近邻原则进行分类。-Sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the
include
- 一种基于字符骨架中封闭曲线特征和纵向线条特征的两级初分类算法,将原来含有62个元素的待识别字符集比较平均的分成了三个子集,降低了后续处理的难度。-Research on Printed Character Recognition System Based on BP Neutral Network
pattern-recognition-simulation
- 用mushrooms数据对模式识别课程讲述的各种模式分类方法[线性分类,Bayesian分类,Parzen窗,KNN]和特征选择和降维方法[PCA,LDA]进行了模拟,并给出了各类分类方法的结果,-It s the simulations about linear classification ,Bayesian ,Parzen and KNN of pattern recognition .And ,It gives the results.
TextMining
- 介绍自动文本分类的一个ppt,详细的介绍了自动文本分类的特征提取,分类算法以及评估。-Introduced an automatic text classification ppt, a detailed introduction of automatic text categorization of feature extraction, classification algorithms, as well as assessment.
fum
- 标准化后进行PCA特征提取,然后聚类分类-After standardized PCA feature extraction, clustering and classification
SVM2004
- 支持向量机工具箱,内含demo程序,教你如何训练SVM和进行特征分类-Suppot Vector Machine (SVM) toolbox, Author Dr Gavin C. Cawley
mboxplot
- 自己在进行分类时写的用于多变量分布浏览,特征选择的函数,可快速查看各特征分布情况,便于后续特征选择-use to display character value
OpenGL_Extensions_Tutorial
- 基于纹理特征参数的图像处理及分类的方法,该方法效果好,易理解。-Texture features based on image processing and classification parameters of the method effective, easy to understand.
face
- 完整的表情识别系统一般包括人脸表情图像捕获、预处理、人脸检测与定位、 人脸分割与归一化、人脸表情特征提取、人脸表情识别。本文着重研究了人脸表 情特征提取、特征选择及表情分类等关键问题,并提出了一些改进的方法,同时 进行了仿真实验-Complete expression recognition systems typically include facial expression image capture, preprocessing, face detection and loca
bp
- bp神经网络实现数据分类——基于语音信号的特征分类-bp neural network data classification- classification based on the characteristics of the speech signal
HaarPclassifiers
- 利用vs2008编写的利用haar特征分类器实现的人脸检测程序-Use vs2008 prepared by the use haar feature classifier implemented face detection process
直方图特征提取
- 直方图统计特征提取,用于特征识别,分类识别等方面
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
案例1 BP神经网络的数据分类-语音特征信号分类
- 基于BP神经网络的聚类分析数据分类例如语音信号识别(Clustering analysis based on BP neural network)
利用Hog特征和SVM分类器进行行人检测
- 利用Hog特征和SVM分类器进行行人检测(Using Hog features and SVM classifiers for pedestrian detection)
AR模型特征提取及分类
- AR特征提取,可用于不同类别数据的分类特征提取(AR feature extraction algorithm)
BP神经网络的数据分类-语音特征信号分类
- bp网络用于数据分类,一类语音特征信号的分类,以供参考学习(BP network is used for data classification, and a class of speech feature signals is classified for reference learning)
HOG图像分类
- 使用HOG算法提取特征以此来对图像进行分类(Use HOG to extract features to classify images)
红酒分类-点-2归一化后
- 将三类不同红酒进行自动分类,其中每种酒具有13类特征(Three kinds of different red wine are classified automatically, each of which has 13 types.)
信息论特征选择KDD Code
- 基于信息熵的特征选择算法,评价每个属性与分类的关联信息,评价属性,进行特征选择(Feature selection algorithm based on Information Entropy)