搜索资源列表
stprtool.rar
- 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines,
wine
- pca-kmeans聚类 先将数据(wine,uci数据集)降维处理，在进行聚类-pca-kmeans clustering use the data of uci:wine.
cvap3.7
- a pca implementation of a algorithme of clustering some data to use this clusters in a futur treatements.
hokashyap_pca
- ho-kashyap pca normalization clustering algorithm
pca
- This file implement pca algorithm for dimension reduction and clustering techniques. Principle Component Analysis, Dimension reduction, clustering.
cluster-1.50.tar
- 数据挖掘聚类算法，kmeans，HC，pca， SOMs-Data mining clustering algorithm, kmeans, HC, pca, SOMs
Machine-Learning-Toolkit-Examples
- labview关于机器学习的案例，来源于NI lab，有多种方法：BP、Kernel pca、clustering、svm等等，值得大家学习。-labview on the case of machine learning, from the NI lab, there are a number of ways: BP, the Kernel the pca, clustering, svm, etc., is worth learning.
PatternRecognition
- (1)Bayes分类 已知N=9， =3，n=2，C=3,问x= 应属于哪一类？ (2)聚类 使用c－均值聚类算法在IRIS数据上进行聚类分析 (3)鉴别分析 在ORL或Yale标准人脸数据库上完成模式识别任务。 用pca与基于核的pca（Kpca）方法完成人脸图像的重构与识别试验。-(1) Bayes classification Known N = 9, = 3, n = 2, C = 3, x = should ask which cat
network-clustering-network-intrusion
- 基于pca有监督kohonen网络的网络入侵聚类。里面包含有原代码和说明文件。-Based on pca supervised kohonen network clustering network intrusion
pca
- pca主成分分析，在多变量选择上效果较好，对数据的主成分进行分析，常用于分类、聚类、实验数据处理-pca principal component analysis in multivariate selection effect is good, principal component analysis of data, often used in classification, clustering, experimental data processing
pca
- pca算法原理介绍和仿真代码，主要用于数据的聚类，代码时用于图像上的聚类过程，聚类效果很好，就是有点慢-pca algorithm introduces the principle and simulation code, mainly for clustering data, a clustering process images on-time code, clustering works well, is a bit slow
lm
- 各种对图像的分类和聚类的方法，kmeas、knn、pca等，还有几种数据处理中的窗函数-Variety of image classification and clustering methods, kmeas, knn, pca, etc., there are several data processing window function
K-Means-clustering-and-pca
- 此代码为matlab代码，分为两个部分。第一部分实现K均值聚类算法应用它来压缩图像。在第二部分中，你将使用主成份分析法pca来实现人脸图像的低维表示。 -This code for the matlab code, is divided into two parts. The first part of the implementation of the K means clustering algorithm to compress the image. In the second par
xvvjukna
- 是本科毕设的题目，计算加权加速度,可实现对二维数据的聚类，信号维数的估计，结合pca的尺度不变特征变换(SIFT)算法。- The title of the commercial is undergraduate course you Weighted acceleration, Can realize the two-dimensional data clustering, Signal dimension estimates, Combined with pca scale invarian
iyqwzwdp
- 一种流形学习算法（很好用），ICA（主分量分析）算法和程序，用于信号特征提取、信号消噪，基于欧几里得距离的聚类分析，FIR 底通和带通滤波器和IIR 底通和带通滤波器，有借鉴意义哦，是学习pca特征提取的很好的学习资料，基于负熵最大的独立分量分析。-A fluid manifold learning algorithm (good use), ICA (Principal Component Analysis) algorithm and procedures, For feature extr
yevhpizm
- 这个有中文注释，看得明白，借鉴了主成分分析算法（pca），关于小波的matlab复合分析，用MATLAB实现动态聚类或迭代自组织数据分析，IDW距离反比加权方法，有CDF三角函数曲线/三维曲线图，正确率可以达到98％。-The Chinese have a comment, understand it, It draws on principal component analysis algorithm (pca), Matlab wavelet analysis on complex, Usi
tcyzmqyb
- 正确率可以达到98％，通过虚拟阵元进行DOA估计，连续相位调制信号(CPM)产生，微分方程组数值解方法，包含CV、CA、Single、当前、恒转弯速率、转弯模型，基于欧几里得距离的聚类分析，考虑雨衰 阴影 和多径影响，借鉴了主成分分析算法（pca）。- Accuracy can reach 98 , Conducted through virtual array DOA estimation, Continuous phase modulation signal (CPM) to produce
bweqrmdv
- 相关分析过程的matlab方法，随机调制信号下的模拟ppm，调试通过可以使用，利用自然梯度算法，结合pca的尺度不变特征变换(SIFT)算法，处理信号的时频分析，D-S证据理论数据融合，可实现对二维数据的聚类。- Correlation analysis process matlab method, Random ppm modulated analog signal under Debugging can be used, Use of natural gradient algorithm,
giegie_v68
- 借鉴了主成分分析算法（pca），基于欧几里得距离的聚类分析，music高阶谱分析算法。- It draws on principal component analysis algorithm (pca), clustering analysis based on Euclidean distance, music higher order spectral analysis algorithm.
machine-learning-ex7
- Andrew Ng Cousera 机器学习K-means勇于图像压缩 以及主成分分析pca用在人脸识别，源代码以及说明文档。(Andrew Ng Cousera machine learning , the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dime