Description: This MATLAB code produces simple vowels by simulating vocal tract as a transmission line of different cross section areas at different distances from the glottis. Platform: |
Size: 1024 |
Author: |
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Description: 关于图像配准中的交叉相关的实现，可以直接调用。-About Image Registration in the implementation of cross-correlation can be directly called. Platform: |
Size: 5120 |
Author:sai |
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Description: 交互小波分析及其一致性分析，用于分析两个相关的时间序列之间存在的相互关系及一致性。-Cross-wavelet analysis and consistency analysis for the analysis of time series between the two related to the relationship between the existence and consistency. Platform: |
Size: 215040 |
Author:孙 |
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Description: 是一種線性方成的分類器。SVM透過統計的方式將雜亂的資料以NN的方式分成兩類，以便處理。LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM. -Main features of LIBLINEAR include
Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
Cross validation for model selection
Probability estimates (logistic regression only)
Weights for unbalanced data
MATLAB/Octave, Java interfaces
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Size: 521216 |
Author:陳彥霖 |
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Description: 偏最小二乘算法 利用交叉验证算法计算应提取成分的个数，本程序给出了交叉验证的发放以及回归系数的算法-Partial Least Squares algorithm using cross-validation the number of components to be extracted, this procedure gives the distribution as well as cross-validation regression coefficient of the algorithm Platform: |
Size: 1024 |
Author:zhaowumian |
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Description: 寻找相应的特点,在一副图片的基础是许多光流、立体的视觉和图像间的配准算法。一个发现匹配方法简单,取一小块一个图像,计算其滑动互相与其他的形象,并找到一个高峰。此表格提供了课堂的实现方法。-Finding corresponding features in a pair of images is the basis of many optic flow, stereo vision and image registration algorithms. One straightforward approach to finding a match is to take a small patch of one image, compute its sliding cross-correlation with the other image, and find a peak. This submission supplies a class which implements this method.
Platform: |
Size: 776192 |
Author:王羽 |
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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: 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: 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: 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. Platform: |
Size: 6144 |
Author:Gang Fu |
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Description: 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
数, 以交叉验证误差为评价准则, 利用粒子群优化( P SO ) 进化搜索LSSV M 预测模型的最优超参数与嵌入维
数, 同时通过矩阵变换原理提高交叉验证过程的计算效率, 并最终建立优化后的L SSVM 预测模型。航空发
动机排气温度( EGT ) 预测实例表明, 该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测
精度高, 适用于航空发动机状态时间序列预测。- T o deal wit h the difficulty of selecting an appro pr iate embedding dimension for aeroeng ine co ndition
time series predictio n, a metho d based o n least squar es suppo rt vecto r machine ( L SSVM ) with ada ptive em
bedding dimension is pro po sed. I n the method, the embedding dimensio n is identified as a parameter that af
fects the accuracy o f the aer oengine condition time series predictio n par ticle sw arm o ptimizat ion ( P SO) is ap
plied to optimize the hyperpar ameter s and embedding dimension of the L SSV M pr edict ion model cro ssv alida
tion is applied to evaluate the perfo rmance o f the L SSVM predictio n mo del and matr ix tr ansfo rm is applied to
the L SSVM pr ediction model tr aining to accelerate the crossvalidation evaluation pro cess. Ex periments on an
aeroengine ex haust g as t emperatur e ( EGT ) predictio n demonst rates that the metho d is hig hly effective in em
bedding dimension selection. In compar ison w ith co nv Platform: |
Size: 342016 |
Author: |
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Description: 基于pls对光谱分析 包括数据读取，小波变换PCA分析，PLS建模，交叉验证-Pls include data on the spectrum based on reads, wavelet transform PCA analysis, PLS modeling, cross-validation Platform: |
Size: 4096 |
Author:liu |
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Description: 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 Platform: |
Size: 8192 |
Author:wujing |
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Description: 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) Platform: |
Size: 9216 |
Author:even_1
|
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Description: 利用matlab实现贝叶斯分类，采用10折10次交叉验证法选取训练集和测试集，进行循环测试，最后返回准确率为0.9184.另外，文件内含数据源。(The Bias classification is realized by MATLAB, and the training set and test set are selected by 90% off 10 times cross validation method, and the cycle test is carried out. Finally, the accuracy rate is 0.9184., and the file contains the data source.) Platform: |
Size: 3072 |
Author:网安贝 |
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