Description: 与其它高级语言相比较，MATLAB具有独特的优势：(1) MATLAB是一种跨平台的数学语言。(2) MATLAB是一种超高级语言。MATLAB平台本身是用C语言写成的，所以在编程效率，程序的可读性、可靠性和可移植性上远远超过了常规的高级语言。(3) MATLAB语法简单，编程风格接近数学语言描述，是数学算法开发和验证的最佳工具。(4) MATLAB计算精度很高。与其它语言相比，MATLAB对计算机内存、硬盘空间的要求也是比较高的。-Compared with other high-level languages , MATLAB has unique advantages: (1) MATLAB is a cross-platform language of mathematics. (2) MATLAB is a super-high-level language. MATLAB platform itself is written in C, so the programming efficiency, readability, reliability and portability far more than the regular high-level language. (3) MATLAB syntax is simple, close to the mathematical language to describe the style of programming is a mathematical algorithm development and validation of the best tools. (4) MATLAB computing high accuracy. Compared with other languages , MATLAB computer memory, disk space requirements are relatively high. Platform: |
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Author:zn |
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Description: 使用交叉验证对KNN算法和SVM算法进行参数优选，其中KNN使用matlab自带的knnclassify，svm使用LIBsvm-Using cross validation for the choosing of hyperparameter for KNN and SVM Platform: |
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Author:天钦 |
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Description: How to install the libsvm for MATLAB on Unix machine
Linear-kernel SVM for binary classification
kernel SVM for binary classification
cross validation for C and Gamma
multi-class SVM: one-vs-rest (OVR)
More ready-to-use matlab example
Available matlab codes to download Platform: |
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Author:hoangthom |
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Description: 1. 使用matlab自带的人脸识别工具（Viola-Jones算法）找出人脸的位置，并裁剪出人脸区域。
2. 使用Gabor滤波器识别出人脸的局部特征及纹理。
3. 训练一个SVM进行表情分类。
4. 交叉验证得到表情分类正确率为83.3 。
操作说明和系统描述请见ReadMe.-1. Using matlab with face detection tool (Viola-Jones algorithm) to find the location of a human face, and cut out the face region.
2. Use Gabor filter to identify the face local feature and texture.
3. Adopt SVM to train a facial expression classifier.
4. Use cross-validation to exam the classification accuracy: 83.3 . Platform: |
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Author:冯溢 |
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Description: SVM Light工具箱 Matlab接口，已经编译好，可直接用(SVMlight, by Joachims, is one of the most widely used SVM classification and regression package. It has a fast optimization algorithm, can be applied to very large datasets, and has a very efficient implementation of the leave-one-out cross-validation. Distributed as C++ source and binaries for Linux, Windows, Cygwin, and Solaris. Kernels: polynomial, radial basis function, and neural (tanh).) Platform: |
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Author:ym89413
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Description: LWP是一种Matlab / Octave工具箱实现局部加权多项式回归（也被称为局部回归/局部加权散点平滑/黄土/ LOWESS和核平滑）。使用此工具箱，您可以使用九个具有度量窗口宽度或最近邻窗口宽度的任意一个内核来拟合任意维度的数据的局部多项式。还提供了一个优化内核带宽的函数。优化可采用留一交叉验证，GCV，AICC、AIC，FPE，T，执行，或单独的验证数据。鲁棒拟合也可用。(LWP is a Matlab/Octave toolbox implementing Locally Weighted Polynomial regression (also known as Local Regression / Locally Weighted Scatterplot Smoothing / LOESS / LOWESS and Kernel Smoothing). With this toolbox you can fit local polynomials of any degree using one of the nine kernels with metric window widths or nearest neighbor window widths to data of any dimensionality. A function for optimization of the kernel bandwidth is also available. The optimization can be performed using Leave-One-Out Cross-Validation, GCV, AICC, AIC, FPE, T, S, or separate validation data. Robust fitting is available as well.) Platform: |
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Author:baidudu |
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Description: 采用SVM高斯核，对样本进行分类，输入训练集和测试集，输出SVM分类准确率，采用10折交叉验证(This matlab code uses the SVM(support Vector Machine)to classify, and as the same time it uses the cross validation mathod.) Platform: |
Size: 1024 |
Author:HUIKEY |
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Description: 本程序为广义交叉验证（GCV）的matlab代码，在一些场合有比交叉验证（OCV）更好的特性(This program is a generalized cross validation (GCV) matlab code, in some cases than cross validation (OCV) better characteristics) Platform: |
Size: 1024 |
Author:lzu666 |
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Description: 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.) Platform: |
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Author:pluto1888 |
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Search - cross-validation matlab