Search - k cross validation

Search - k cross validation - List

[AI-NN-PR] AI_Blood DL : 0: 本次大作业利用K‐近邻（K‐Nearest Neighbor）算法，为给定的训练数据集构造了分类器，并在测试数据集上进行分类预测，同时计算了Accuracy、Precision、Recall和F‐measure，利用 10‐fold的实验方法进行交叉验证。-The big job to use K-neighbor (K-Nearest Neighbor) algorithm, for a given set of training data classifier is constructed, and the test data sets to classify forecasts, while the calculation of the Accuracy, Precision, Recall and F-measure , using 10-fold cross-validation of experimental methods.
Date : Size : 987kb User : andy
[AI-NN-PR] Ex1 DL : 0: 模式识别某次课程的作业，完成了高斯分布下的两种贝叶斯分类器,以及非参数的K近邻、Parzen窗方法，采用UCI机器学习数据库中的某些数据作为样本，使用交叉验证方法确定参数-Pattern recognition of a particular course work, completed under the two Gaussian Bayesian classifier, and the non-parametric K-nearest neighbor, Parzen window method, using UCI machine learning database as sample some of the data, using cross-validation method to determine the parameters
Date : Size : 1.09mb User : libo
[matlab] K-Fold_CV_Tool DL : 0: MATLAB cross-validation tool for classification and regression v0.1 FEATURES: + K-fold cross validation. + Arbitrary train and prediction functions with parameters can be used. + Arbitrary loss function can be used. + Wrappers for KNN, SVM, GLM, robust regression and decision trees. + Wrappers for RMSE, MAD and misclassification loss functions.
Date : Size : 3kb User : milk
[Algorithm] Density_Estimation DL : 0: 分别采用GMM和KDE对Iris数据集进行密度建模，并进行对比。通过EM算法来确定GMM参数，通过交叉验证来确定K值-GMM and KDE respectively Iris data set of density modeling, and compared. GMM by EM algorithm to determine the parameters of K determined by the value of cross-validation
Date : Size : 2.81mb User : 高进
[matlab] FeatureSelection_MachineLearning DL : 0: Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves.-Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves.
Date : Size : 6kb User : Gang Fu
[matlab] cross-validation DL : 0: matlab交叉验证cross Validation，把样本集分为训练集和测试集，防止网络出现过拟合，提高网络的泛化能力和预测精度-cross Validation for matlab，to estimate the test accuracy,training accuray and validation accuracy of a neural network
Date : Size : 1kb User : 周杰伦
[AI-NN-PR] bpcross DL : 1: 一个matlab写的bp人工神经网络程序，参数优化采用交叉验证办法-Write a matlab bp artificial neural network program, parameter optimization using cross-validation method
Date : Size : 98kb User : lifei
[AI-NN-PR] fastsvm1 DL : 0: 机器学习大牛Dale Schuurmans写的多类SVMs的快速实现算法，可以自己修改核函数，通过K-fold cross validation训练得到最优参数，分类效果很好-Machine learning large cattle Dale Schuurmans write multi-class SVMs fast algorithm, can modify the kernel function, the optimal parameters through K-fold cross validation training classification works well
Date : Size : 3kb User : wang
[AI-NN-PR] knn DL : 0: k近邻分类器分类包括PCA功能归一化并且带有交叉检验功能-k nearest neighbor classifiers including PCA function normalized and with cross-validation function
Date : Size : 139kb User : 朱戎生
[matlab] create_kfolds_balanced_train_test DL : 0: Divide/Create data into K-Folds training and testing data which are balanced basing on the labels. It is for k-fold cross validation.
Date : Size : 1kb User : tund
[matlab] crossvalidation_svm DL : 0: matlab编写的调整svm参数的程序，其中cross是主程序，另两个是自己编写的svm核函数，如果要用matlab自带的核函数就把-t的值改成2即可。Ytrain是标记矩阵，Xtrain是特征矩阵，都由用户自己导入。可利用k倍交叉验证来选择最优的c参数。k可自行更改。-svm matlab prepared to adjust the parameters of the program, which cross the main program, and the other two are themselves prepared svm kernel function, if you use the value of the kernel function matlab own put-t can be changed to 2. Ytrain is labeled matrix, Xtrain is characteristic matrix, imported by the users themselves. K-fold cross-validation can be used to select the optimal c parameter. k can make changes.
Date : Size : 1.65mb User : wang
[matlab] ANN-k-fold-cross-validation DL : 0: ann k-fold cross validation matlab cone
Date : Size : 1kb User : amit
[AI-NN-PR] K---NN DL : 0: 以著名的wine数据作为实验样本。包括k-NN算法，交叉验证，PCA降维等。-With the famous wine data as experimental samples.K- NN algorithm, cross validation, PCA dimension reduction, etc.
Date : Size : 18kb User : Zhuzhiwei
[AI-NN-PR] stprtool_v2.12 DL : 0: 统计模式识别工具箱(STPRTool 版本2.12 2013-09-12) 功能有线性判别函数、特征提取、密度估计和聚类、支持向量机、贝叶斯分类器、交叉验证等-Statistical Pattern Recognition Toolbox Methods: Fisher,PCA,GMM,K-means,SVM,Bayes classifier,Cross-validation,KNN,etc.
Date : Size : 5.76mb User : lee
[AI-NN-PR] knndemo DL : 0: K近邻分类器，实现了对iris数据集的分类，并且使用了交叉验证的方法，来验证求得的最优的K值。-K-nearest neighbor classifier to achieve the classification of iris data set and cross-validation of the method used to verify the optimal value of K obtained.
Date : Size : 2kb User :
[AI-NN-PR] code_1 DL : 0: 在机器学习中利用欧氏距离设计一个KNN分类器，实现五折交叉验证，并用PCA进行降维-Develop a k-NN classifier with Euclidean distance and simple voting.Perform 5-fold cross validation, find out which k performs the best (in terms of accuracy)。Use PCA to reduce the dimensionality to 6, then perform 2) again.
Date : Size : 6kb User : yuanxin
[matlab] SRGTSToolbox DL : 5: 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.)
Date : Size : 354kb User : pluto1888