搜索资源列表
smlr.m
- 编写的多元回归的交叉验证程序 可供做预测模型的同仁参考-Prepared by the multiple regression of cross-validation procedure
libsvm_src_2.6NOTE
- LIBSVM是台湾大学林智仁(Lin Chih-Jen)副教授等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包，他不但提供了编译好的可在Windows系列系统的执行文件，还提供了源代码，方便改进、修改以及在其它操作系统上应用；该软件还有一个特点，就是对SVM所涉及的参数调节相对比较少，提供了很多的默认参数，利用这些默认参数就可以解决很多问题；并且提供了交互检验(Cross Validation)的功能。该软件包可以在http://www.csie.ntu.edu.tw/~c
libsvm-mat-2[1].9-11
- LIBSVM是台湾大学林智仁(Lin Chih-Jen)副教授等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包，他不但提供了编译好的可在Windows系列系统的执行文件，还提供了源代码，方便改进、修改以及在其它操作系统上应用；该软件还有一个特点，就是对SVM所涉及的参数调节相对比较少，提供了很多的默认参数，利用这些默认参数就可以解决很多问题；并且提供了交互检验(Cross Validation)的功能。该软件包可以在http://www.csie.ntu.edu.tw/~c
KNN
- Implement the K nearest neighbor algorithm by your own instead of using available software. 2. Use K-fold cross validation to generate training and testing datasets. You should try different K values (3~8) to see how they affect your result. 3. T
scoreMapping5.21
- 使用最小二乘法利用特征进行打分映射，并进行交叉验证-Using the least squares method using the mapping feature for scoring, and conduct cross-validation
classifier
- 用matlab实现Part1. 实现一个k近邻分类器，Part 2.实现一个最小二乘分类器，Part 3.实现一个支持向量机分类器，Part 4.在不同数据集上使用交叉验证选择各个算法的参数-Part1. Achieve a k-nearest neighbor classifier, Part 2. Achieve a least-squares classifier, Part 3. Implement a support vector machine classifier, Part 4.
cross_validation
- 基于交叉验证法小波降噪程序，主要应用白噪声-Cross-validation method based on wavelet noise reduction program, the main application of white noise
Naive-Bayes-Text-Classification
- 使用Python实现朴素贝叶斯分类，文件夹中附带数据集。实现了NB算法，并进行5倍交叉验证-Naive Bayes classifier using the Python implementation, the folder with the data set. NB implements the algorithm, and 5-fold cross-validation
PLSRG
- 用于自动计算X和Y矩阵的PLS回归。生成的各种表格一XLS文件存储。其中交叉验证内置，但是可能会有问题-Used to automatically calculate the X and Y matrices PLS regression. A variety of forms generated XLS file storage. Where cross-validation built-in, but there may be a problem
lvq
- code for lvq and split the data to be train and test by k-fold cross validation with k=5
crossvalidation
- Cross-Validation procedure
Classification
- Classification code using cross validation
newbayes
- 基于Python的贝叶斯实现，同时包含改进贝叶斯算法，同时采用采用留存交叉验证进行验证。-Python-based Bayesian implementations, including improved Bayesian algorithm, while using cross-validation using retained for verification.
GRNN
- 基于BP和GRNN神经网络的粮食产量预测研究，通过训练样本和测试样本的交叉验证，实现粮食产量预测效果的最佳化-Prediction of Grain Yield BP and GRNN based training through cross-validation and testing samples, to achieve the best effect of the Grain Production Forecast
knn-softsvm
- knn，最小二乘，softsvm分类器的matlab实现，以及简单的交叉验证等-knn, least squares, soft svm classifier matlab implementation, and simple cross-validation, etc.
交叉验证
- 输入一组数据，从从中随机把样本分为测试数据与验证数据(Random selection of samples from cross validation in a set of access data)
光滑样条
- 光滑样条拟合，采用交叉验证选取df ，观测选取不同df值时的拟合效果图(The code of smooth splines,first we find the best df by cross-validation,then we choose different df to compare the result.)
plscv
- pls交叉验证，可自行设定K-folder交叉验证(pls cross validation)
CSVM
- 基本svm函数代码库，包含基本svm用到的函数，以及交叉验证，精度测试等等(Including the functions used by the basic SVM, as well as cross validation, precision testing, and so on)
matlab
- 对于一个具体的数据，用交叉验证进行分类，随机森林进行训练，用AUC,AUPR,Precision评价分类器的性能(For a specific data, use cross validation to classify, train random forests, evaluate the performance of the classifier with AUC, AUPR, and Precision.)