搜索资源列表
rbf
- 向量机算法中的核函数 向量机算法中的核函数-SVM kernel function
rbfSVM
- 基于RBF径向基核函数实现SVM支撑矢量机算法，-SVM algorithm based on RBF kernel
svm-rbf-demo
- 基于RBF核函数的最小二乘支持向量机演示程序，对学习支持向量机有帮助-Based on RBF kernel functions, the least squares support vector machine (SVM) demo application to the study of support vector machine have help
file
- adboosting 弱分类和回归程序，通过参数选择，确定用哪类功能，内核回归用RBF神经网络-adboosting weak classification and regression procedures, parameter selection, determined by the types of functions, kernel regression with RBF neural network
demofun
- This a simple demo, solving a simple regression task using LS-SVMlab. A dataset is constructed in the right formatting. The data are represented as matrices where each row contains one datapoint. In order to make an LS-SVM model, we need 2 extra para
Gaussian
- 通过 Gaussian kernel说明非线性回归分析的算法-Gaussian kernel smoother, Gaussian kernel-based linear regression, RBF kernel regression
SVMcg
- 支持向量机参数选择，利用网格搜索法确定支持向量机的惩罚因子和RBF核参数-Support vector machine (SVM) parameters choice, the grid search method is used to determine the punishment factor and RBF kernel parameters of support vector machine (SVM)
SVM
- classify using one-against-one approach, SVM with linear, 3rd degree poly,RBF 7 kernel
KPCA
- 另尝试编写的一个代码如下，实现对3类数据特征空间的聚类，如下图，红、绿、蓝三种颜色分别表示3类数据，经过rbf核映射到新的空间后，分别聚成了3类-This technique takes advantage of the kernel trick that can be used in PCA. This is a tutorial only and is slow for large data sets. In line 30 the kernel can be changed. Any Ke
HW6
- non linear svm with libsvm with RBF kernel
matrbf
- 在MATLAB中调用核函数为rbf的svm，通过训练数据对测试数据进行分类（仅适用于二分类数据）(In MATLAB, use the kernel function rvf svm, through the training data on the test data classification (only for two categories of data))
D2dataset-master
- Perform SVM and test results a) For now, assume that the main scr ipt is grid_search.IT performs a grid search for SVM using RBF kernel , for the 2 parameters C and gamma .Criterios for choosing the right values is the F1 score and accuracy of the
svr
- RBF核函数编写和svr的matlab编写(RBF kernel function writing)