搜索资源列表
SVM--SVM
- 支持向量机，用于分类，含训练集与测试集。里面含有六个源程序-support vector machines for the classification, with the training set and test sets. It contains six source
classification
- 该程序包实现了几个常用的模式识别分类器算法，包括K近邻分类器KNN、线性判别方程LDF分类器、二次判别方程QDF分类器、RDA规则判别分析分类器、MQDF改进二次判别方程分类器、SVM支持向量机分类器。 主程序中还有接口调用举例，压缩包中还有两个测试数据集文件。-The package to achieve a number of commonly used pattern recognition classifier algorithms, including K neighbor class
rbf
- 实现了RBF网络，已载入训练和测试样本，可根据需要将其更换-Achieved a RBF network, has been printed in the training and test samples may be needed to replace
LS-SVM
- 基于LS-SVM的入侵检测模型与实时测试平台研究-LS-SVM based intrusion detection model and real-time test platform for research
svm-EM
- SVM(支持向量机）和EM（最大熵）文本分类算法，压缩包中包括了测试文本（环境类和计算机类），词典，停用词表等。-SVM (support vector machine) and EM (maximum entropy) text classification algorithm, compressed package includes test text (environmental and computer), dictionary, thesaurus, such as disabled.
svm
- 用MATLAB编写的分类回归程序，通用。并给出了实例对分类效果进行检验。-MATLAB prepared using classification and regression procedures, General. And give examples of the classification results from such a test.
SVMbyQuadprog
- This is a support vector machine program developed based on quadprog. Polynomial and RBF kernel are supported. Test it by executing example.m with supported data.
SVM
- 用C语言自己编写基于特定训练数据和测试数据的SVM程序，并用AUC对其评估-I have written using C language based on the specific training data and test data of the SVM procedure, and their assessment of AUC
M-K
- M-K test for climate research-M-K test
SVMNR
- 支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。 -Support Vector Machine and BP neural network, ev
SVM-Matlab
- matlab写的SVM分类，tranning和test分开，可直接使用。-matlab write SVM classification, tranning and test separately, can be used directly.
SVM
- SVM工具箱，做分类和回归用很好。6.5下测试通过。-SVM Toolbox, to do with a good classification and regression. 6.5 under test.
Based-on-SVM-speaker-recognition
- 基于SVM的文本无关说话人识别算法研究，本文在最后用Matlab程序实现了一个基于支持向量机的说话人识别系统试验平台。并根据对参试者进行的大量身份测试试验，总结系统的各方面性能和分析存在的问题，为进一步研究提供了方向和宝贵的经验。 -SVM-based text independent speaker recognition algorithm, the paper used in the final implementation of a Matlab program based on
Matlab-svm-BP-compare
- 支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。-SVM and BP neural networks, although non-linear regr
SVM
- 一个MATLAB语言写的SVM算法测试例，便于理解SVM二分类方法的实际含义(A MATLAB language written SVM algorithm test case, easy to understand the SVM two classification method of practical significance)
SVM算法二分类
- 将支持向量机（SVM）用于模式识别，解决二分类问题，程序中包含训练集和测试集。(The support vector machine (SVM) is used for pattern recognition to solve the dichotomy problem, which includes training set and test set.)
SVM
- 训练集：trainset(); 分别取bedroom(1:5,:)和forse(1:5,:)作为训练集； 测试集：testset(); 分别取bedroom(6:10,:)和forse(6:10,:)作为测试集； 标签集：label(); 取bedroom的数据为正类标签为1；forse的数据为负类标签为-1.(Training set: trainset (); take bedroom (1:5,) and forse (1:5,:) as the training set; Tes
SVM
- 该算法用Visual Studio编写 ，用于实现对样本的训练以及测试，并可以转换成matlab语言，直接调用子程序(The algorithm is written in Visual Studio, which is used to train and test the sample, and can be converted into a matlab language and directly invoked the subroutine.)
GBDT+SVM
- 使用机器学习中的SVM，GBDT算法构建分类模型，做分类预测。并且对测试结果评估，模型保存。(Use SVM and GBDT algorithm in machine learning to build classification model and do classification prediction. And evaluate the test results and save the model.)
PSO优化SVM参数
- 粒子群算法优化libsvm参数，可以自行修改，亲测可用(Particle Swarm Optimization (PSO) optimizes the parameters of libsvm, which can be modified by itself and can be used for pro-test.)