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[Other resource] SVM_luzhenbo

Description: 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 -a toolbox : LS_SVMlab Classification_LS_SVMlab.m-over classification Regression_LS_SVMlab.m-function fitting 2, toolbox : OSU_SVM3.00 Classification_OSU_SVM.m-over classification 3, toolbox : stprtool \\ svm Classification_stprtool.m-over category Categories 4, toolbox : SVM_SteveGunn Classification_SVM_SteveGun n.m-II classification Regression_SVM_SteveGunn.m-fitting function
Platform: | Size: 2697321 | Author: 赵阳 | Hits:

[Graph Recognize] BPC++

Description: Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM regression. It also provides an automatic model selection tool for C-SVM classification. This document explains the use of libsvm. -Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classificatio n, nu-SVM classification, one-class-SVM. epsilon - SVM regression. and nu-SVM regression. It also provides an auto matic model selection tool for C-SVM classific ation. This document explains the use of libsvm .
Platform: | Size: 7900 | Author: pangjiufeng | Hits:

[Other resource] n_psvm

Description: n类PSVm 算法程序，相对于svm运算速度较快-n Class PSVm algorithm procedures, in relation to computing speed svm
Platform: | Size: 2993 | Author: zhangxi | Hits:

[Other resource] libsvm-2.85-dense

Description: LIBSVM源码。LIBSVM 是台湾大学林智仁(Chih-Jen Lin)博士等开发设计的一个操作简单、 易于使用、快速有效的通用SVM 软件包，可以解决分类问题（包括C- SVC、 n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM ） 等问题，提供了线性、多项式、径向基和S形函数四种常用的核函数供选择，可以有效地解决多类问题、交叉验证选择参数、对不平衡样本加权、多类问题的概率估计等。
Platform: | Size: 24502 | Author: 刘铁军 | Hits:

[Other resource] svm_v0.55beta

Description: 最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, \"The Nature of Statistical Learning Theory\", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, \"Fast training of support vector machines using sequential minimal optimization\", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, \"Estimating the Generalization Performance of a SVM Efficiently\", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it.
Platform: | Size: 172130 | Author: 金星 | Hits:

[AI-NN-PR] svm_v0.55beta

Description: 最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it.
Platform: | Size: 172032 | Author: 金星 | Hits:

[matlab] SVM_luzhenbo

Description: 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 -a toolbox : LS_SVMlab Classification_LS_SVMlab.m-over classification Regression_LS_SVMlab.m-function fitting 2, toolbox : OSU_SVM3.00 Classification_OSU_SVM.m-over classification 3, toolbox : stprtool \ svm Classification_stprtool.m-over category Categories 4, toolbox : SVM_SteveGunn Classification_SVM_SteveGun n.m-II classification Regression_SVM_SteveGunn.m-fitting function
Platform: | Size: 2697216 | Author: 赵阳 | Hits:

[Graph Recognize] BPC++

Description: Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM regression. It also provides an automatic model selection tool for C-SVM classification. This document explains the use of libsvm. -Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classificatio n, nu-SVM classification, one-class-SVM. epsilon- SVM regression. and nu-SVM regression. It also provides an auto matic model selection tool for C-SVM classific ation. This document explains the use of libsvm .
Platform: | Size: 7168 | Author: pangjiufeng | Hits:

[matlab] n_psvm

Description: n类PSVm 算法程序，相对于svm运算速度较快-n Class PSVm algorithm procedures, in relation to computing speed svm
Platform: | Size: 3072 | Author: zhangxi | Hits:

[AI-NN-PR] libsvm-2.85-dense

Description: LIBSVM源码。LIBSVM 是台湾大学林智仁(Chih-Jen Lin)博士等开发设计的一个操作简单、 易于使用、快速有效的通用SVM 软件包，可以解决分类问题（包括C- SVC、 n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM ） 等问题，提供了线性、多项式、径向基和S形函数四种常用的核函数供选择，可以有效地解决多类问题、交叉验证选择参数、对不平衡样本加权、多类问题的概率估计等。-LIBSVM source. LIBSVM is林智仁Taiwan University (Chih-Jen Lin) Dr. develop design a simple, easy to use, fast and effective generic SVM software package, can solve the classification problems (including the C-SVC, n- SVC), regression ( including e- SVR, n- SVR) as well as the distribution of estimates (one-class-SVM) and so on, provides a linear, polynomial, radial basis function and the S-shaped kernel function of four commonly used for selection, can effectively to solve a wide range of issues, cross-validation to choose the parameters of the imbalance in the weighted sample, multi-category probability estimation.
Platform: | Size: 24576 | Author: 刘铁军 | Hits:

[matlab] libsvm-2.88

Description: LIBSVM 是台湾大学林智仁 (Chih-Jen Lin) 博士等开发设计的一个操作简单、易于使用、快速有效的通用 SVM 软件包，可以解决分类问题（包括 C- SVC 、n - SVC ）、回归问题（包括 e - SVR 、 n - SVR ）以及分布估计（ one-class-SVM ）等问题，提供了线性、多项式、径向基和 S 形函数四种常用的核函数供选择，可以有效地解决多类问题、交叉验证选择参数、对不平衡样本加权、多类问题的概率估计等。-LIBSVM is林智仁Taiwan University (Chih-Jen Lin) Dr. develop design a simple, easy to use, fast and effective generic SVM software package, can solve the classification problems (including the C-SVC, n- SVC), regression ( including e- SVR, n- SVR) as well as the distribution of estimates (one-class-SVM) and so on, provides a linear, polynomial, radial basis function and the S-shaped kernel function of four commonly used for selection, can effectively to solve a wide range of issues, cross-validation to choose the parameters of the imbalance in the weighted sample, multi-category probability estimation.
Platform: | Size: 518144 | Author: 小潘 | Hits:

[Windows Develop] gp425win32

Description: 易于使用、快速有效的通用SVM 软件包，可以解决分类问题（包括C- SVC、 n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM -Easy to use, fast and effective generic SVM software package can solve the classification problems (including the C-SVC, n- SVC), regression (including e- SVR, n- SVR) as well as the distribution of estimates (one-class-SVM
Platform: | Size: 3941376 | Author: yuanmin | Hits:

[matlab] libsvm-2.89

Description: LIBSVM 是台湾大学林智仁(Chih-Jen Lin)博士等开发设计的一个操作简单、易于使用、快速有效的通用SVM 软件包，可以解决分类问题（包括C- SVC、n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM ）等问题，提供了线性、多项式、径向基和S形函数四种常用的核函数供选择，可以有效地解决多类问题、交叉验证选择参数、对不平衡样本加权、多类问题的概率估计等。 2.89版本是09年刚更新的一个版本。-LIBSVM
Platform: | Size: 566272 | Author: woyaofei | Hits:

[AI-NN-PR] LS-SVMlab1.5

Description: SVM 软件包，可以解决分类问题（包括C- SVC、n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM ）等问题-SVM software package can solve the classification problems (including the C-SVC, n- SVC), regression (including e- SVR, n- SVR) as well as the distribution of estimates (one-class-SVM) and other issues
Platform: | Size: 32768 | Author: hanzeyu | Hits:

[MultiLanguage] webcat

Description: 这是一个100 ％纯Java库，您可以使用适用于N元 分析技术的过程分为文本文件。 该计划包括几个不同的分类算法， namelly 支持向量机，贝叶斯Logistic回归，神经网络分类和文本压缩 算法。如支持向量机和贝叶斯Logistic回归，一个 “一对一” 用于多类分类。更详细的说明这些学习算法和可用的选项，请提供的javadocs 。-It is a 100 pure Java library that you can use to apply N-Gram analysis techniques to the process of categorizing text files. The package includes several different categorization algorithms, namelly SVMs, Bayesian Logistic Regression, NN classification and a text compression based algorithm. In the case of SVM and Bayesian Logistic Regression, a "one-against-one" apprach is used for multiclass classification. For a more detailed description of these learning algorithms and the available options please consult the supplied javadocs.
Platform: | Size: 838656 | Author: liwen | Hits:

[Mathimatics-Numerical algorithms] svm4

Description: 　-s svm类型：SVM设置类型(默认0) 　　0 -- C-SVC 　　1 --v-SVC 　　2 – 一类SVM 　　3 -- e -SVR 　　4 -- v-SVR 　　-t 核函数类型：核函数设置类型(默认2) 　　0 – 线性：u v 　　1 – 多项式：(r*u v + coef0)^degree 　　2 – RBF函数：exp(-r|u-v|^2) 　　3 –sigmoid：tanh(r*u v + coef0) 　　-d degree：核函数中的degree设置(针对多项式核函数)(默认3) 　　-g r(gama)：核函数中的gamma函数设置(针对多项式/rbf/sigmoid核函数)(默认1/ k) 　　-r coef0：核函数中的coef0设置(针对多项式/sigmoid核函数)((默认0) 　　-c cost：设置C-SVC，e -SVR和v-SVR的参数(损失函数)(默认1) 　　-n nu：设置v-SVC，一类SVM和v- SVR的参数(默认0.5) 　　-p p：设置e -SVR 中损失函数p的值(默认0.1) 　　-m cachesize：设置cache内存大小，以MB为单位(默认40) 　　-e eps：设置允许的终止判据(默认0.001) 　　-h shrinking：是否使用启发式，0或1(默认1) 　　-wi weight：设置第几类的参数C为weight*C(C-SVC中的C)(默认1) 　　-v n: n-fold交互检验模式，n为fold的个数，必须大于等于2--s svm_type : set type of SVM (default 0) 0-- C-SVC 1-- nu-SVC 2-- one-class SVM 3-- epsilon-SVR 4-- nu-SVR -t kernel_type : set type of kernel function (default 2) 0-- linear: u *v 1-- polynomial: (gamma*u *v+ coef0)^degree 2-- radial basis function: exp(-gamma*|u-v|^2) 3-- sigmoid: tanh(gamma*u *v+ coef0) 4-- precomputed kernel (kernel values in training_instance_matrix) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in kernel function (default 1/k) -r coef0 : set coef0 in kernel function (default 0) -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default 100) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1) -b
Platform: | Size: 17408 | Author: little863 | Hits:

[matlab] Introduction-to-Matlab-SVM

Description: Matlab支持向量机导论（中文版） 作者：【英】 Nello Cristianini ， John Shawe-Taylor 译者：李国正，王猛，曾华军 出版社：电子工业出版社 版次：2004年3月第1次印刷-Introduction to Matlab Support Vector Machine (Chinese Edition) Author: n. Nello Cristianini, John Shawe-Taylor Translator: Li Guo-Zheng, Wang Meng, who Huajun Publisher: Electronic Industry Press Revision: March 2004 1st Printing
Platform: | Size: 4893696 | Author: 李凯 | Hits:

[Special Effects] svm

Description: SVM平台，操作简单、易于使用的通用SVM 软件包，可以解决分类问题（包括C- SVC、n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM ）等问题，提供了线性、多项式、径向基和S 形函数四种常用的核函数供选择。-SVM platform is a simple, easy to use, versatile SVM software package can solve classification problems (including C-SVC, n- SVC), regression (including e- SVR, n- SVR) and distribution estimation (one-class-SVM) and other issues, providing a linear, polynomial, radial basis functions and the S-shaped four commonly used kernel functions for selection.
Platform: | Size: 633856 | Author: 凡轩 | Hits:

[AI-NN-PR] activity-recognition-based-on-SVM

Description: 基于支持向量机的人类活动识别，以日常生活中的10个活动进行识别。-Support Vector Machine (SVM) was first proposed in 1995 by Cortes and Vapnik [15] for solving classification and regression problems. The solving strategy of SVM on the multiple classification problems is commonly “one to one” strategy, whose basic thought is that the classification problems with N categories will be decomposed into ()1/2 NN− binary classification problems to deal with, and meantime, ()1/2 NN− training classifiers needed to be trained. In the training process, any two categories of all the N categories would be selected as one group. Assume that we have five categories, and these categories are labeled ,,, , ABC DE. Figure 3 shows the () 551/2 ×− classifiers according to “one to one” tragedy.
Platform: | Size: 28672 | Author: guolei | Hits:

[matlab] SVM-master

Description: Improved Support Vector Machine in MATLAB. Some other functions included as we-This is a improved SVM learner coding in MATLAB. I constructed a new N-Dimensional Hessian Matrix and Gradient Vector builder in costFcn.m. Check the comments in the function for more information.
Platform: | Size: 4096 | Author: saisai | Hits:
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