Description: New in this version:
Support for multi-class pattern recognition using maxwins, pairwise [4] and DAG-SVM [5] algorithms.
A model selection criterion (the xi-alpha bound [6,7] on the leave-one-out cross-validation error).
-New in this version : Support for multi-class pattern recognition u maxwins sing, Pairwise [4] and DAG - SVM [5] algorithms. A mode l selection criterion (the xi-alpha bound [6, 7] on the leave-one-out cross-validation erro r). Platform: |
Size: 43182 |
Author:吴成 |
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Description: Feature scaling for kernel Fisher discriminant analysis using leave-one-out cross validation.
FS-KFDA is a package for implementing feature scaling for kernel fisher discriminant analysis.-Feature scaling for kernel Fisher discrim inant analysis using leave-one-out cross vali dation. FS-KFDA is a package for implementing f eature scaling for kernel fisher discriminant analysis. Platform: |
Size: 511443 |
Author:ihatexlet |
Hits:
Description: New in this version:
Support for multi-class pattern recognition using maxwins, pairwise [4] and DAG-SVM [5] algorithms.
A model selection criterion (the xi-alpha bound [6,7] on the leave-one-out cross-validation error).
-New in this version : Support for multi-class pattern recognition u maxwins sing, Pairwise [4] and DAG- SVM [5] algorithms. A mode l selection criterion (the xi-alpha bound [6, 7] on the leave-one-out cross-validation erro r). Platform: |
Size: 43008 |
Author:吴成 |
Hits:
Description: Feature scaling for kernel Fisher discriminant analysis using leave-one-out cross validation.
FS-KFDA is a package for implementing feature scaling for kernel fisher discriminant analysis.-Feature scaling for kernel Fisher discrim inant analysis using leave-one-out cross vali dation. FS-KFDA is a package for implementing f eature scaling for kernel fisher discriminant analysis. Platform: |
Size: 510976 |
Author: |
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Description: The leave-one-out cross-validation scheme is a method for estimating
％ the average generalization error. When calling
％ [Eloo,H] = loo(NetDef,W1,W2,PHI,Y,trparms) with trparms(1)>0, the network
％ will be retrained a maximum of trparms(1) iterations for each input-output
％ pair in the data set, starting from the initial weights (W1,W2). If
％ trparms(1)=0 an approximation to the loo-estimate based on "linear
％ unlearning" is produced. This is in general less accurate, but is much
％ faster to calculate.-The leave-one-out cross-validation scheme is a method for estimating the average generalization error. When calling [Eloo, H] = loo (NetDef, W1, W2, PHI, Y, trparms) with trparms (1)> 0, the network will be retrained a maximum of trparms (1) iterations for each input-output pair in the data set, starting from the initial weights (W1, W2). If trparms (1) = 0 an approximation to the loo-estimate based on linear unlearning is produced. This is in general less accurate, but is much faster to calculate. Platform: |
Size: 3072 |
Author:张镇 |
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Description: The code implements a probabilstic Neuraol network for classification problems trained with a Leave One Out Cross Validation Scheme in Matlab (version 7 or above). The following toolboxes are required: statidtics, optimization and neural networks. Platform: |
Size: 33792 |
Author:Alfredo/Passos |
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Description: 留一法交叉检验,用于得到留一法交叉检验系数-Leave one out cross-validation for the leave-one-out cross examination coefficient Platform: |
Size: 1024 |
Author:qieqie |
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Description: LS-SVM Leave-One-Out Cross-Validation Demo
G. C. Cawley, "Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs", Proceedings of the International Joint Conference on Neural Networks (IJCNN-2006), pages 1661-1668, Vancouver, BC, Canada, July 16-21 2006. (pdf)theoval.cmp.uea.ac.uk/~gcc/matlab/
Platform: |
Size: 5120 |
Author:leozajung |
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Description: SVM Light工具箱 Matlab接口，已经编译好，可直接用(SVMlight, by Joachims, is one of the most widely used SVM classification and regression package. It has a fast optimization algorithm, can be applied to very large datasets, and has a very efficient implementation of the leave-one-out cross-validation. Distributed as C++ source and binaries for Linux, Windows, Cygwin, and Solaris. Kernels: polynomial, radial basis function, and neural (tanh).) Platform: |
Size: 66560 |
Author:ym89413
|
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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: SURROGATES工具箱是一个多维函数逼近和优化方法的通用MATLAB库。当前版本包括以下功能：
实验设计：中心复合设计，全因子设计，拉丁超立方体设计，D-optimal和maxmin设计。
代理：克里金法，多项式响应面，径向基神经网络和支持向量回归。
错误和交叉验证的分析：留一法和k折交叉验证，以及经典的错误分析（确定系数，标准误差;均方根误差等;）。
基于代理的优化：高效的全局优化（EGO）算法。
其他能力：通过安全裕度进行全局敏感性分析和保守替代。(SURROGATES Toolbox is a general-purpose MATLAB library of multidimensional function approximation and optimization methods. The current version includes the following capabilities:
Design of experiments: central composite design, full factorial design, Latin hypercube design, D-optimal and maxmin designs.
Surrogates: kriging, polynomial response surface, radial basis neural network, and support vector regression.
Analysis of error and cross validation: leave-one-out and k-fold cross-validation, and classical error analysis (coefficient of determination, standard error; root mean square error; and others).
Surrogate-based optimization: efficient global optimization (EGO) algorithm.
Other capabilities: global sensitivity analysis and conservative surrogates via safety margin.) Platform: |
Size: 362496 |
Author:pluto1888 |
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Search - leave one out cross