Description: Locally weighted polynomial regression LWPR is a popular instance based al gorithm for learning continuous non linear mappings For more than two or three in puts and for more than a few thousand dat apoints the computational expense of pre dictions is daunting We discuss drawbacks with previous approaches to dealing with this problem Platform: |
Size: 745472 |
Author:陈月辉 |
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Description: 强局部加权回归算法由Cleveland[7]提出,主要利用局部观测数据对欲拟合点进行多项式加权拟合,并用最小二乘法进行估计.它综合了传统的局部多项式拟合,局部加权回归以及具有强鲁棒性的拟合过程
-Strong locally weighted regression algorithm by Cleveland [7] proposed, mainly using local observational data points on the polynomial fitting For weighted fitting, and estimated by least square method. It combines the traditional local polynomial fitting, locally weighted regression as well as strong robustness of the fitting process Platform: |
Size: 3072 |
Author:wanghuaqiu |
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Description: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. -Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. Platform: |
Size: 777216 |
Author:duckur |
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Description: this program compare the Locally Weighted Linear Regression with three diferrent kernel function (gaussian, logistic basis, and Reciprocal Multiquadric) also compare locally weighted by simple Linear Regression. Platform: |
Size: 9216 |
Author:maisam |
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Description: Locally weighted regression (LWR) is a memory-based method that performs a regression around a point of interest using only training data that are ``local to that point. One recent study demonstrated that LWR was suitable for real-time control by constructing an LWR-based system that learned a difficult juggling task.-Locally weighted regression (LWR) is a memory-based method that performs a regression around a point of interest using only training data that are ``local to that point. One recent study demonstrated that LWR was suitable for real-time control by constructing an LWR-based system that learned a difficult juggling task. Platform: |
Size: 2048 |
Author:肖邦 |
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Description: LOWESS, Locally Weighted Scatterplot Smoothing for linear and non-linear data (enhanced)- LOWESS- Locally Weighted Scatterplot Smoothing
Modifications:
This regression will work on linear and non-linear relationships between
X and Y. Platform: |
Size: 43008 |
Author:shijie |
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Description: 斯坦福大学公开课第三课中涉及的局部加权线性回归，用matlab实现了在不同带宽下的对比-Stanford open class third lesson involved locally weighted linear regression, using matlab to achieve a comparison of different bandwidths Platform: |
Size: 4096 |
Author:wkh |
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Description: 代码为Matlab环境下开发的局部加权线性回归算法，以绘图方式表达结果。-Matlab code for locally weighted linear regression algorithm under development environment to graphically express the results. Platform: |
Size: 1024 |
Author:陈静 |
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Description: 局部加权线性回归，解决非线性回归问题，可以在Matlab直接测试。-This code is the Locally Weighted Linear Regression,which is mainly used to cope with non-linear regression. Platform: |
Size: 1024 |
Author:李飞洋 |
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Description: 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 Weighted Polynomial regression (also known as Local Regression / Locally Weighted Scatterplot Smoothing / LOESS / LOWESS and Kernel Smoothing). With this toolbox you can fit local polynomials of any degree using one of the nine kernels with metric window widths or nearest neighbor window widths to data of any dimensionality. A function for optimization of the kernel bandwidth is also available. The optimization can be performed using Leave-One-Out Cross-Validation, GCV, AICC, AIC, FPE, T, S, or separate validation data. Robust fitting is available as well.) Platform: |
Size: 5120 |
Author:baidudu |
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Description: 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 Regression / Locally Weighted Scatterplot Smoothing / LOESS / LOWESS and Kernel Smoothing). With this toolbox you can fit local polynomials of any degree using one of the nine kernels with metric window widths or nearest neighbor window widths to data of any dimensionality. A function for optimization of the kernel bandwidth is also available.) Platform: |
Size: 2048 |
Author:baidudu |
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Description: 局部加权多项式回归的目的是解决全球行为模型的表现不好或不能有效地应用于不必要的努力。LWP是一种非参数回归方法，是通过低阶多项式对数据子集进行逐点拟合局部。(Locally Weighted Polynomial regression is designed to address situations in which models of global behaviour do not perform well or cannot be effectively applied without undue effort. LWP is a nonparametric regression method that is carried out by pointwise fitting of low-degree polynomials to localized subsets of the data.) Platform: |
Size: 4096 |
Author:baidudu |
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