搜索资源列表
papericao
- 基本的ICA算法代码,进行有效的特征提取，是一种降维方法。-the matlab code of ICA
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.
lda_1.3.2.tar
- lda算法，用于特征降维，可以将特征映射到更加易于区分的空间-FISHER DISCRIMINANT ANALYSIS WITH KERNELS
qoumui_V1.0
- 使用大量的有限元法求解偏微分方程，用于特征降维，特征融合，相关分析等,用于信号特征提取、信号消噪。- Using a large number of finite element method to solve partial differential equations, For feature reduction, feature fusion, correlation analysis, For feature extraction, signal de-noising.
bangbou
- matlab小波分析程序，用于特征降维，特征融合，相关分析等,采用的是通用的平面波展开法。- matlab wavelet analysis program, For feature reduction, feature fusion, correlation analysis, Using common plane wave expansion method.
genban
- 可以实现模式识别领域的数据的分类及回归，包括回归分析和概率统计，用于特征降维，特征融合，相关分析等。- You can achieve data classification and regression pattern recognition, Including regression analysis and probability and statistics, For feature reduction, feature fusion, correlation analysis.
heiyou
- 用于特征降维，特征融合，相关分析等,可实现对二维数据的聚类，基于kaiser窗的双谱线插值FFT谐波分析。- For feature reduction, feature fusion, correlation analysis, Can realize the two-dimensional data clustering, Dual-line interpolation FFT harmonic analysis kaiser windows.
bounie
- 进行逐步线性回归，用于特征降维，特征融合，相关分析等,插值与拟合的matlab实现。- Stepwise linear regression, For feature reduction, feature fusion, correlation analysis, Interpolation and fitting matlab implementation.
miefun
- 一种基于多文档得图像合并技术，光纤无线通信系统中传输性能的研究，用于特征降维，特征融合，相关分析等。- Based on multi-document image obtained combining technique, Fiber Transmission wireless communication system performance, For feature reduction, feature fusion, correlation analysis.
ganmun
- 一个很有用的程序，用于特征降维，特征融合，相关分析等,考虑雨衰 阴影 和多径影响。- A very useful program, For feature reduction, feature fusion, correlation analysis, Consider shadow rain attenuation and multipath effect.
fiegun
- 用于特征降维，特征融合，相关分析等,有详细的注释，基于kaiser窗的双谱线插值FFT谐波分析。- For feature reduction, feature fusion, correlation analysis, There are detailed notes, Dual-line interpolation FFT harmonic analysis kaiser windows.
toufui_v42
- 一种噪声辅助数据分析方法,基于混沌的模拟退火算法，用于特征降维，特征融合，相关分析等。- A noise auxiliary data analysis method, Chaos-based simulated annealing algorithm, For feature reduction, feature fusion, correlation analysis.
genghei
- AHP层次分析法计算判断矩阵的最大特征值,基于多相结构的信道化接收机，用于特征降维，特征融合，相关分析等。- Calculate the maximum eigenvalue judgment matrix of AHP, Channelized receiver based on multi-phase structure, For feature reduction, feature fusion, correlation analysis.
pingsui_v28
- 用于特征降维，特征融合，相关分析等,用MATLAB实现的压缩传感，BP神经网络用于函数拟合与模式识别。- For feature reduction, feature fusion, correlation analysis, Using MATLAB compressed sensing, BP neural network function fitting and pattern recognition.
bangmun
- PLS部分最小二乘工具箱,用于特征降维，特征融合，相关分析等,雅克比迭代求解线性方程组课设。- PLS PLS toolbox, For feature reduction, feature fusion, correlation analysis, Jacobi iteration for solving linear equations class-based.
laogan_v52
- 使用混沌与分形分析的例程，课程设计时编写的matlab程序代码，用于特征降维，特征融合，相关分析等。- Use Chaos and fractal analysis routines, Course designed to prepare the matlab program code, For feature reduction, feature fusion, correlation analysis.
benban
- 仿真效果非常好，用于特征降维，特征融合，相关分析等,可以得到很精确的幅值、频率、相位估计。- Simulation of the effect is very good, For feature reduction, feature fusion, correlation analysis, You can get a very accurate amplitude, frequency, phase estimation.
PCA实现特征降维
- pca和_fase_pca实现特征降维，两种算法都有所改进，特别是pca可以根据自己的需要进行相应的改进，代码清晰易懂，希望对你有帮助。(PCA and _fase_pca to achieve feature reduction, the two algorithms have improved, especially PCA can be improved according to their needs, the code is clear and easy to understand,
降维与特征选择
- 在machine learning中，特征降维和特征选择是两个常见的概念，在应用machine learning来解决问题的论文中经常会出现。 对于这两个概念，很多初学者可能不是很清楚他们的区别。很多人都以为特征降维和特征选择的目的都是使数据的维数降低，所以以为它们是一样的，曾经我也这么以为，这个概念上的误区也就导致了我后面对问题的认识不够深入。后来得到老师的指点才彻底搞清楚了两者的关系，现总结出来与大家分享。(Feature reduction and feature sele
abnormal - 副本
- 利用小波变化提取数据的前五层特征值，主要是提取特征降维(Using wavelet transform to extract the first five levels of eigenvalues of data.)