搜索资源列表
svm1
- trainning an svm using non-linaear kernel function. the code can be simulated under Matlab
aa2nt
- trainning and testing an svm using non-linaear kernel function. the code can be simulated under Matlab
NaiveBayesClassifier.m
- I use Matlab 2008a which does not support Naive Bayes Classifier. scr ipt supports normal and kernel distributions. Statistics toolbox for 2008a version is used in the scr ipt. Also includes function for confusionmat
demo_libsvm
- How to install the libsvm for MATLAB on Unix machine Linear-kernel SVM for binary classification kernel SVM for binary classification cross validation for C and Gamma multi-class SVM: one-vs-rest (OVR) More ready-to-use matlab example Ava
LS_SVR
- This scr ipt simply used Gaussian and Morlet Kernel to show you how apply LS-SVR in MATLAB
ml_least_square_method_p036
- Matlab高斯核模型的l2约束最小二乘法学习-Constrained least squares learning with Gaussian kernel model
GMM_KDE
- malab代做|hslogic|基于MATLAB的GMM和KDE核估计得目标跟踪仿真-Malab generation to do |hslogic| based MATLAB GMM and KDE kernel estimation target tracking simulation
2SVR
- 支持向量机回归的ｍａｔｌａｂ版本，里面包含高斯核函数等一系列常用的核函数。(Support vector regression matlab version, which contains the Gauss kernel function and a series of commonly used kernel functions.)
TriadQuad
- 这个文 件夹 包括 文 章 Fast and Robust Kernel Generators for Star Trackers 中的 matlab 角本 ，是文 章中的数据图和列表产生代码。(This folder contains matlab files that generated plots and tables for the "Fast and Robust Kernel Generators for Star Trackers" journal art
aok
- 本程序用matlab编写，基于自适应最优核的时频分析(Time frequency analysis based on adaptive optimal kernel)
kernel_eca-master
- Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.(We introduce kernel entropy component analysis (kernel ECA) as a new method fo
SVM_Mdl.mat
- These files are matlab source code for price forecasting for smart meter hourly data. Step 1 relevant features are selected by Gray Correlation, Random Forest, Relief F algorithms. Then Kernel PCA of features are calculated. Price is predicted by Ker
lwppredict
- LWP是一种Matlab / Octave工具箱实现局部加权多项式回归（也被称为局部回归/局部加权散点平滑/黄土/ LOWESS和核平滑）。使用此工具箱，您可以使用九个具有度量窗口宽度或最近邻窗口宽度的任意一个内核来拟合任意维度的数据的局部多项式。还提供了一个优化内核带宽的函数。优化可采用留一交叉验证，GCV，AICC、AIC，FPE，T，执行，或单独的验证数据。鲁棒拟合也可用。(LWP is a Matlab/Octave toolbox implementing Locally Weight
lwpparams
- LWP是一种Matlab / Octave工具箱实现局部加权多项式回归（也被称为局部回归/局部加权散点平滑/黄土/ LOWESS和核平滑）。使用此工具箱，您可以使用九个具有度量窗口宽度或最近邻窗口宽度的任意一个内核来拟合任意维度的数据的局部多项式。还提供了一个优化内核带宽的函数。(LWP is a Matlab/Octave toolbox implementing Locally Weighted Polynomial regression (also known as Local Regre
lwpeval
- LWP是一种Matlab / Octave工具箱实现局部加权多项式回归（也被称为局部回归/局部加权散点平滑/黄土/ LOWESS和核平滑）。(LWP is a Matlab/Octave toolbox implementing Locally Weighted Polynomial regression (also known as Local Regression / Locally Weighted Scatterplot Smoothing / LOESS / LOWESS and Ke
svr
- RBF核函数编写和svr的matlab编写(RBF kernel function writing)
function_kpca
- 使用核函数，在matlab环境下实现非线性主成分分析(Using kernel function to realize nonlinear principal component analysis in Matlab environment.)
sobel
- 由Verilog编写在FPGA实现sobel算法应用于图像边缘检测，工程文件可在quartus13.1以上版本打开；工程使用到ram、fifo、pll三种ip核，design文件夹下包含ram、fifo、vga控制以及串口收发和sobel算法模块，sim和doc文件夹下分别包含modelsim的仿真模块和仿真结果；测试时将200*200分辨率的图片用matlab文件夹下的matlab脚本压缩、二值化，再将生成文件中数据用串口发给FPGA，边缘检测结果会通过VGA输出。(Written by Ve
Calc_kerls
- volterra series 核函数 matlab 程序(volterra series kernel)
VolSurface
- 波动率曲面matlab实现，可应用于期权市场上的任意期权。(The function VolSurface.m will then: - compute and output the Black-Scholes implied volatility (this will be a matrix). - get and plot the corresponding volatility surface using a kernel (Gaussian) density estimation.)