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LIBSVM是台湾大学林智仁(Lin Chih-Jen)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，利用这些默认参数可以解决很多问题；并提供了交互检验(Cross Validation)的功能。该软件可以解决C-SVM、ν-SVM、ε-SVR和ν-SVR等问题，包括基于一对一算法的多类模式
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K近邻分类器，实现了对iris数据集的分类，并且使用了交叉验证的方法，来验证求得的最优的K值。-K-nearest neighbor classifier to achieve the classification of iris data set and cross-validation of the method used to verify the optimal value of K obtained.
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属于机器学习的范畴，通过输入训练样本，通过分类或线性回归得到标签的假设性函数-The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library that has been specifically designed for real-time gesture recognition.
In addition to a comprehensive C++ AP
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通过KNN分类法，根据酒的某些性状信息对酒进行分类。然后，通过交叉验证对分类结果进行测试。最后，对数据进行主成分分析，再进行分类。-By KNN classification, according to certain traits information wine wine classification. Then, through cross-validation of the classification tested. Finally, principal component analy
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基于交叉验证的支持向量机算法，程序内可实现选择最佳参数，并对输入数据分类输出-Cross-validation based on support vector machine algorithm, can be realized within the program to the best parameters, and input data classification output
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一个用python编写的带交叉验证的SVM分类程序，前提是必须正确安装里面所用到的Python库-Written in python with a cross-validation of SVM classification procedures, must be properly installed inside the premise is used by the Python library
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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
Ava
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算法流程：选定训练集和测试集-数据预处理-交叉验证选择最佳参数-分类准确率-预测-利用最佳参数训练SVM-Algorithm flow: selected training set and test set- data preprocessing- cross-validation selection of the best parameters- classification accuracy- prediction- training SVM using the best parameter
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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
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svm 的参数优化，利用交叉验证法选择最优参数c g，最终提高训练集的分类准确率,更好的提高分类器性能-Svm parameter optimization, the use of cross-validation method to the optimal parameter c g, and ultimately improve the training set classification accuracy，better improve the classifier performan
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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
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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)
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二分类支持向量机交叉验证调参并统计分类准确率(Cross validation of two classification support vector machines and statistical classification accuracy)
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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. Fin
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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)
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