Description: 里面有两个实验，包括PPT和实验的代码，可以帮助一些想学习神经网络的朋友一个大忙。-We now have an easy script (easy.py) for users who know NOTHING about svm. It makes everything automatic--from data scaling to parameter selection.
The parameter selection tool grid.py generates the following contour of cross-validation Platform: |
Size: 933888 |
Author:baiyunqiu |
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Description: matlab下偏最小二乘回归模型
交叉有效性验证，及奇异点发现
-matlab partial least-squares regression model of cross-validation, and the singular points found Platform: |
Size: 3072 |
Author:lina |
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Description: 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. Platform: |
Size: 1010688 |
Author:andy |
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Description: estimate the test accuracy,training accuray and validation accuracy of a neural network with 10-fold cross validation.-estimate the test accuracy,training accuray and validation accuracy of a neural network with 10-fold cross validation. Platform: |
Size: 1024 |
Author:sak |
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Description: 图像分类中的交叉验证方法，比如说，一个训练集集合，为了得到其中参数的较准确值，就可以使用此类算法-Image Classification Based on cross-validation method, for example, a collection of training set, in order to obtain more accurate values of these parameters, you can use these algorithms Platform: |
Size: 1024 |
Author:苏晓蕾 |
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Description: 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。-LIBSVM Taiwan University, Lin Zhiren (Lin Chih-Jen) and associate development and design of a simple, easy to use and fast and efficient SVM pattern recognition and regression of the package, he can not only provide a compiled implementation of the system in the Windows family of documents , also provides the source code to facilitate the improvement, modification and other operating systems applications the software has a feature that the parameters of the SVM involves relatively small adjustment to provide a lot of default parameters, use the default parameters on can solve many problems and to provide a cross-validation (Cross Validation) feature. Platform: |
Size: 472064 |
Author:朱业林 |
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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: 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 Platform: |
Size: 1138688 |
Author:libo |
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Description: Feature Selection using Matlab.
The DEMO includes 5 feature selection algorithms:
• Sequential Forward Selection (SFS)
• Sequential Floating Forward Selection (SFFS)
• Sequential Backward Selection (SBS)
• Sequential Floating Backward Selection (SFBS)
• ReliefF
Two CCR estimation methods:
• Cross-validation
• Resubstitution
After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.
Figure: Upper panel is the pattern x feature matrix
Lower panel left are the features selected
Lower panel right is the CCR curve during feature selection steps
Right panel is the classification results of some patterns.
This software was developed using Matlab 7.5 and Windows XP.
Copyright: D. Ververidis and C.Kotropoulos
AIIA Lab, Thessaloniki, Greece,
jimver@aiia.csd.auth.gr
costas@aiia.csd.auth.gr-Feature Selection using Matlab.
The DEMO includes 5 feature selection algorithms:
• Sequential Forward Selection (SFS)
• Sequential Floating Forward Selection (SFFS)
• Sequential Backward Selection (SBS)
• Sequential Floating Backward Selection (SFBS)
• ReliefF
Two CCR estimation methods:
• Cross-validation
• Resubstitution
After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.
Figure: Upper panel is the pattern x feature matrix
Lower panel left are the features selected
Lower panel right is the CCR curve during feature selection steps
Right panel is the classification results of some patterns.
This software was developed using Matlab 7.5 and Windows XP.
Copyright: D. Ververidis and C.Kotropoulos
AIIA Lab, Thessaloniki, Greece,
jimver@aiia.csd.auth.gr
costas@aiia.csd.auth.gr Platform: |
Size: 3283968 |
Author:driftinwind |
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Description: 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. Platform: |
Size: 3072 |
Author:milk |
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Description: Naive Bayes training and testing with 10-fold cross validation. Train and test data files provided. Platform: |
Size: 13163520 |
Author:sehmetia |
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Description: 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 Platform: |
Size: 3072 |
Author:weiqier |
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Description: 介绍了交叉验证的基本概念和常见问题。不是源码，但是编程用对新手很有用。-Introduces the basic concepts of cross-validation and frequently asked questions. Not the source, but the programming is useful for the novice to use. Platform: |
Size: 17408 |
Author:sun |
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Search - cross validation