搜索资源列表
matlab-PLS
- 很好用，用matlab编的偏最小二乘法，直接在matlab里就可以用。另外带有PLS讲解-Good use, with the partial least squares matlab compiled directly in matlab can be used inside. In addition to explain with PLS
pls
- 偏最小二乘程序 标准化 求矩阵的最大特征值及对应特征向量-pls
1
- 用matlab实现偏最小二乘算法,其中包含一个例子的源代码。-Partial least squares algorithm using matlab
NIPALS-Matlab
- 基于NIPALS算法的偏线性最小二乘(PLS)Matlab程序-NIPALS linear algorithm based on partial least squares (PLS) Matlab program
qoupun_v81
- 包含收发两个客户端程序，这是一个好用的频偏估计算法的matlab仿真程序,PLS部分最小二乘工具箱。- Transceiver contains two client programs, This is a useful frequency estimation algorithm matlab simulation program, PLS PLS toolbox.
beiging
- 这是一个好用的频偏估计算法的matlab仿真程序,在matlab环境中自动识别连通区域的大小,最小二乘回归分析算法。- This is a useful frequency estimation algorithm matlab simulation program, Automatic identification in the matlab environment the size of the connected area, Least-squares regression analysi
pls
- 偏最小二乘回归，里边有若干相关因素，分别输入历史数据，并与待预测量进行偏最小二乘回归，拟合出方程，从而进行预测(Partial least squares regression)
PLSMATLAB
- 偏最小二乘回归的MATLAB实现，数据是贝壳的光谱数据，附带了平滑预处理程序(MATLAB implementation of partial least squares regression)
crossval
- matlab偏最小二乘算法中交叉有效性准则的调用函数(Calling function of cross validity criterion in partial least squares algorithm)
osccalc
- matlab编程偏最小二乘算法调用函数，交叉有效性准则(Calling function of cross validity criterion in partial least squares algorithm)
plsdan
- matlab偏最小二乘算法多通道输入单通道输出程序(Partial least squares algorithm, multi-channel input single channel output program.)
plsduo
- matlab偏最小二乘算法多通道输入多通道输出程序(Partial least squares algorithm, multi-channel input multi-channel output program.)
simpls
- matlab偏最小二乘算法调用函数simpls.m文件(Partial least squares algorithm call function)
PLS_Toolbo
- pls_toolbox工具箱可以解决统计领域多个问题的matlab求解问题，比如偏最小二乘问题、多向主元问题、主元分析等等，集成的工具箱方便，而且不需要验证码！(The pls_toolbox toolbox can solve the MATLAB problem of multiple problems in the field of statistics, such as partial least squares, multidirectional principal element,
PLSUVE
- 基于偏最小二乘回归的matlab中无信息变量消除算法的特征选择(Feature Selection of No Information Variable Elimination Algorithm in Matlab Based on Partial Least Squares Regression)