Description: 对支持向量机svm算法的改进算法，改算法简单，但只能分为两类。-On support vector machine algorithm SVM algorithm to algorithm is simple, but can only be divided into two categories. Platform: |
Size: 38912 |
Author:赵蕾 |
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Description: 这里实现了基于四种SVM工具箱的分类与回归算法:
1、工具箱：LS_SVMlab
Classification_LS_SVMlab.m - 多类分类
Regression_LS_SVMlab.m - 函数拟合
2、工具箱：OSU_SVM3.00
Classification_OSU_SVM.m - 多类分类
3、工具箱：stprtool\svm
Classification_stprtool.m - 多类分类
4、工具箱：SVM_SteveGunn
Classification_SVM_SteveGunn.m - 二类分类
Regression_SVM_SteveGunn.m - 函数拟合
更详细的相关函数说明请通过help命令查看！-Here the realization of the four SVM toolbox based on the classification and regression algorithm: 1, Toolbox: LS_SVMlabClassification_LS_SVMlab.m- Multiclass Classification Regression_LS_SVMlab.m- function fitting 2, the toolbox: OSU_SVM3.00Classification_OSU_SVM.m- Multiclass Classification 3, Toolbox: stprtoolsvmClassification_stprtool.m- Multiclass Classification 4 toolbox: SVM_SteveGunnClassification_SVM_SteveGunn.m- II Category Regression_SVM_SteveGunn.m- function fitting a more detailed explanation of the correlation function through the help command to view! Platform: |
Size: 2740224 |
Author:杨愚根 |
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Description: 有向无环图支持向量(DAG-SVMS)多类分类方法,是一种新的多类分类方法。该方法采用了最小超球体类包含作为层次分类依据。试验结果表明,采用该方法进行多类分类,跟已有的分类方法相比有更高的分类精度。
-Directed acyclic graph support vector (DAG-SVMS) multi-category classification methods, is a new multi-category classification methods. The method uses the smallest category of super-sphere that contains the level of classification as a basis. The experimental results show that using the method of multiclass classification with the classification method has been compared to a higher classification accuracy. Platform: |
Size: 5120 |
Author:苏苏 |
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Description: svm(支持向量机)分类算法本质上是二类分类器，实现多类分类的方法一般是将多类分类看作是多个一对多的二类分类器。本程序就是基于svmlight的svm多类分类器实现。对分类感兴趣的用户请参照。配合中文分词（参见我上传的程序），可实现中文多类分本分类。-svm (support vector machine) classification algorithm is essentially a second-class classifier, multi-category classification methods in general is to multiclass classification is more than one-to-many as second-class classifier. This procedure is based on many types of svmlight of SVM classifiers to achieve. Classification please refer to interested users. With the Chinese word segmentation (see my uploaded program), can be realized at the Chinese multi-category classification. Platform: |
Size: 566272 |
Author:maming |
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Description: 这里实现了四种SVM工具箱的分类与回归算法
1、工具箱：LS_SVMlab
Classification_LS_SVMlab.m - 多类分类
Regression_LS_SVMlab.m - 函数拟合
2、工具箱：OSU_SVM3.00
Classification_OSU_SVM.m - 多类分类
3、工具箱：stprtool\svm
Classification_stprtool.m - 多类分类
4、工具箱：SVM_SteveGunn
Classification_SVM_SteveGunn.m - 二类分类
Regression_SVM_SteveGunn.m - 函数拟合
觉得好就帮我顶一下帖子，要不沉了！
-Here to realize the four SVM toolbox Classification and Regression Algorithm 1, Toolbox: LS_SVMlabClassification_LS_SVMlab.m- Multiclass Classification Regression_LS_SVMlab.m- function fitting 2, the toolbox: OSU_SVM3.00Classification_OSU_SVM.m- Multiclass Classification 3, Toolbox : stprtoolsvmClassification_stprtool.m- Multiclass Classification 4, Toolbox: SVM_SteveGunnClassification_SVM_SteveGunn.m- II Category Regression_SVM_SteveGunn.m- function fitting to help me feel good on top of you post, or heavy! Platform: |
Size: 232448 |
Author:fgqqd |
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Description: 有基于 matlav的svm软件工具，感觉挺好用的。里面有很多实例，稍加修改就可以使用。例如三分类问题：
1、输入三类数据xapp yapp
2、选择多类分类方法（一对多或一对一或m-svm）
3、设置参数
4、调用训练函数得到向量机参数
5、输入测试数据，得到预期结果。
-There is matlav based on the SVM-based software tools, I feel pretty useless. Inside there is a lot of examples you can use a little modification. Categories such as three questions: 1, three types of data input xapp yapp 2, selection multiclass classification methods (one-to-many or one-on-one or m-svm) 3, set the parameters of 4, call the training function to be vector machine parameters 5, enter test data, the expected results. Platform: |
Size: 373760 |
Author:Misa |
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Description: 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines-
This section should give the reader a quick overview of the methods implemented in
STPRtool.
• Analysis of linear discriminant function: Perceptron algorithm and multiclass
modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection
of known algorithms solving the Generalized Anderson’s Task.
• Feature extraction: Linear Discriminant Analysis. Principal Component Analysis
(PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis.
• Probability distribution estimation and clustering: Gaussian Mixture
Models. Expectation-Maximization algorithm. Minimax probability estimation.
K-means clustering.
• Support Vector and other Kernel Machines: Sequential Minimal Optimizer
(SMO). Matlab Optimization toolbox based algorithms. Interface to the
SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers.
Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell-
Demyanov-Molozenov algorithm Platform: |
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Author:查日东 |
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Description: 支持向量机的源代码，可以实现分类和回归分析。-ctions on Intelligent Systems and Technology
ABSTRACT LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
we show formulations used in LIBSVM: C-support vector classifica-tion (C-SVC), ν-support vector classification (ν-SVC), distribution estimation (one-class SVM), -support vector regression (-SVR), and ν-support vector regression
(ν-SVR). We discuss the implementation of solving quadratic problems Platform: |
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Author:guolei |
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Search - SVM matlab multiclass