搜索资源列表
svm_toolbox
- 支持向量机工具箱，其中包含MATLAB演示程序和一些基本的函数（计算核函数的函数、支持向量机训练函数和参数选择交叉验证函数等）。-SVM Toolbox, which contains MATLAB demo programs and some of the basic functions (Calculation Kernel function, SVM training function and parameter selection cross-validation functions,
crossvalidate
- 基于决策树的n则交叉验证分类器 （决策树程序直接调用matlab中的） crossvalidate.m N则交叉验证程序，N可选 NDT.mat 含9个国际公认标准数据集，已做过标么处理，可直接使用 专业-n Based on Decision Tree is cross-validation classification (decision tree directly call the Matlab) cr ossvalidate.m N is cross-validation
PRAssign
- 脱机手写体识别Matlab源程序 包括特征提取、bayes分类器、K近邻分类及最近邻分类。 Testscr iptRecognition.m：测试代码 scr iptFeaExtract.m ：特征提取 KNearestEstimate.m ：K近邻估计 NearestEstimate.m : 最近邻估计 BayesTrain.m ：训练bayes分类器 Bayes.m ：测试bayes分类器 CrossValidate.m ：m交叉验证 -Offlin
crossvalind
- matlab的svm中使用的交叉验证函数（kfold），一般libsvm数据包中没有，需要自己加入-The svm matlab to use the cross-validation function (kfold), general packet libsvm no need to add yourself
trnn
- 神经网络训练，应用matlab7NN包，用一个隐藏层使用5折交叉验证。-Training the Neural Network This scr ipt is something that I did for a course at Uni. It uses the Neural Networking package provided with MatLab 7 unfortunately I m not sure if it s available with the earlier ve
Image
- 求图像的熵以及两幅图像间的交叉熵，自己验证可以使用，放心下载！-Entropy, as well as for the image between two images of the cross-entropy, can be used to verify their own, rest assured that download!
Adaptive-Embedding-Dimension
- 嵌入维数自适应最小二乘支持向量机 状态时间序列预测方法 Condition Time Series Prediction Using Least Squares Support Vector Machine with Adaptive Embedding Dimension 针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题, 提出一种基于嵌入维数自适应 最小二乘支持向量机( L SSVM ) 的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重要参
svmclassify
- 使用交叉验证的svm matlab 代码，简单，比较好用-svm classification
SVM_iris
- matlab，K折交叉验证，SVM方法对IRIS分类-K-flod,matlab ,svm method
crossvalidate
- 一个交叉验证的matlab源码，语句简单，清楚，易修改，好实现-Matlab source code, a cross-validation statement is simple, clear, easy to modify, good to achieve
The-data-forecasting-based-on-GRNN
- 通过matlab编程，建立了基于广义神经网络的数据预测，并通过交叉验证进行网络的训练，取得了不错的预测效果-By matlab programming，we established the data forecasting based on generalized regression neural network, and we use corss-validation to train the network,which obtains better forecasting effect.
Grnn-neural-network--Matlab
- Grnn神经网络交叉验证，matlab中可实现代码文档-Grnn nerve network cross-validation, matlab in the can be to achieve the code documentation
ruqinjiance-svm
- matlab源文件，对网络数据进行入侵检测，利用libsvm工具箱，对特征进行分类。内容包括：数据的归一化，参数择优（交叉验证），建立svm模型，性能评价。压缩包内有详细的说明文档。-matlab source files, network data for intrusion detection, to use libsvm toolbox, to classify the characteristics. The contents include: data normalization, p
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.
knn-softsvm-matlab
- matlab的knn网络，最小二乘算法，softsvm分类器实现，以及简单的交叉验证等，三种产检的方法-knn，softsvm matlab
crossvalidation_svm
- 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 t
lasso_webpage_code_data
- 该压缩包主要是关于lasso的matlab程序，其中包含lasso的code,lasso在例子中的应用，以及lemda的交叉验证等。-The archive is mainly about lasso matlab program, which includes lasso the code, lasso in the example application, as well as cross-validation lemda like.
SVM
- SVM分类器的matlab实现，针对提供的花的特征分类，并交叉验证(The matlab implementation of SVM classifier aims at providing the feature classification of flowers and cross validation)
KRR
- 核岭回归算法 输入数据集(需要分开存放训练集和测试集) 利用4重交叉验证法调参 最后输出分类准确率(Kernel ridge regression algorithm Input data set (training set and test set need to be stored separately) Parameter adjustment by 4-fold cross validation Final output classification accuracy)
免疫算法求解配送中心选址问题matlab代码
- 免疫算法求解配送中心选址问题，配送中心向需求点配送货物是供应链中的重要部分.本文以成本最低为目标函数,把距离上限加入到惩罚机制,并根据抗体和抗原之间的亲和力设计自适应交叉和变异概率,把自适应的免疫算法应用到配送中心模型中进行求解,最后通过仿真实验对比验证了算法用在配送中心选址上有较好的效果.(Immune Algorithm is used to solve the location problem of Distribution Center, which is an important pa