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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.
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Size: 296065 |
Author: baolij |
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Description: JSVM核心类库,收集了JAVA进行JSVM开发必用技术进行归纳,在实际项目应用中直接引用相关类库即可现实JSVM相关功能!-JSVM core class library, a collection of Java development for JSVM must use technology into the actual project application directly related libraries can be invoked JSVM related functional reality!
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Size: 44032 |
Author: 周光洪 |
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Description: 多类支持向量分类机，可避免两类支持向量分类机的相关缺点，实现多类分类-Multi-Class Support Vector Machine, we can avoid two kinds of support vector machine classification of defects, multi-category classification
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Size: 1349632 |
Author: 师花 |
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Description: New in this version:
Support for multi-class pattern recognition using maxwins, pairwise [4] and DAG-SVM [5] algorithms.
A model selection criterion (the xi-alpha bound [6,7] on the leave-one-out cross-validation error).
-New in this version : Support for multi-class pattern recognition u maxwins sing, Pairwise [4] and DAG- SVM [5] algorithms. A mode l selection criterion (the xi-alpha bound [6, 7] on the leave-one-out cross-validation erro r).
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Author: 吴成 |
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Description: 我一直覺得 SVM 是個很有趣的東西，不過也一直沒辦法 (mostly 衝堂) 去聽林智仁老師 的 Data mining 跟 SVM 的課； 後來看了一些網路上的文件跟聽 kcwu 講了一下 libsvm 的用法後，就想整理一下，算是對於並不需要知道完整 SVM 理論的人提供使用 libsvm 的入門.-SVM I always think that is something very interesting, but we also have no way (mostly Chong Tong) to listen to the teachers Linzhiren Data mining with SVM's class; Later read some documents on the Internet kcwu listen to speakers with a little libsvm usage, wanted to tidy up, is not for the need to know SVM complete theory for the use of libsvm entry.
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Size: 405504 |
Author: 军 |
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Description: 用C语言实现的最新且最快的SVM源码,可用于解决多类分类问题-C language of the latest and fastest source of SVM can be used to solve the multi-category classification
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Size: 108544 |
Author: zql |
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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 .
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Size: 7168 |
Author: pangjiufeng |
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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.
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Size: 295936 |
Author: baolij |
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Description: 我一直覺得 SVM 是個很有趣的東西，不過也一直沒辦法 (mostly 衝堂) 去聽林智仁老師 的 Data mining 跟 SVM 的課； 後來看了一些網路上的文件跟聽 kcwu 講了一下 libsvm 的用法後，就想整理一下，算是對於並不需要知道完整 SVM 理論的人提供使用 libsvm 的入門。-I always think that SVM is a very interesting thing, however, has been no way to (mostly red hall) to listen to the teacher Lin Zhiren Data mining with SVM class was reading some documents on the web to listen to kcwu talked about with the usage of libsvm later, wanted to tidy up can be considered for the SVM does not need to know the complete theory of entry to provide the use of libsvm.
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Size: 24576 |
Author: Alec |
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Description: In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition
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Author: sofi |
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Description: EEG signal classification by SVM
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Size: 3072 |
Author: Alok |
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Description: The sample application is able to perform both Classification and Regression using Support Vector Machines. It can read Excel spreadsheets and determines the task to be performed depending on the number of the columns in the sheet. If the input table contains two columns (e.g. X and Y) it will be interpreted as a regression problem X –> Y. If the input table contains three columns (e.g. x1, x2 and Y) it will be interpreted as a classification problem <x1,x2> belongs to class Y, Y being either 1 or -1.
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Author: jitesh |
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Description: svm分类的算法 速度比其他的快一点 需要再做比较 交流 希望能得到更多的资料-SVMmulticlass uses the multi-class formulation described in [1], but optimizes it with an algorithm that is very fast in the linear case
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Author: 常虎 |
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Description: MATLAB函数参考手册，查看matlab函数作用以及功能。- SVMLSPex02.m
Two Dimension SVM Problem, Two Class and Separable Situation
Difference with SVMLSPex01.m:
Take the Largrange Function (16)as object function insteads ||W||,
so it need more time than SVMLSex01.m
Method from Christopher J. C. Burges:
"A Tutorial on Support Vector Machines for Pattern Recognition", page 9
Objective: min "f(A)=-sum(ai)+sum[sum(ai*yi*xi*aj*yj*xj)]/2" ,function (16)
Subject to: sum{ai*yi}=0 ,function (15)
and ai>=0 for any i, the particular set of constraints C2 (page 9, line14).
The optimizing variables is "Lagrange Multipliers": A=[a1,a2,...,am],m is the number of total samples.
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Size: 561152 |
Author: 王东东 |
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Description: 用matlab实现非线性支持向量机分类器对多类进行分类。-Using matlab to achieve non-linear support vector machine classifier for multi-class classification.
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Size: 3072 |
Author: rosalyn |
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Description: 该工具箱包括了二种分类,二种回归,以及一种一类支持向量机算法
(1) Main_SVC_C.m --- C_SVC二类分类算法
(2) Main_SVC_Nu.m --- Nu_SVC二类分类算法
(3) Main_SVM_One_Class.m --- One-Class支持向量机
(4) Main_SVR_Epsilon.m --- Epsilon_SVR回归算法
(5) Main_SVR_Nu.m --- Nu_SVR回归算法-The kit includes two kinds of classification, two kinds of regression, and a one-class support vector machine algorithm (1) Main_SVC_C.m--- C_SVC II classification algorithm (2) Main_SVC_Nu.m--- Nu_SVC II classification algorithm (3) Main_SVM_One_Class.m--- One-Class Support Vector Machine (4) Main_SVR_Epsilon.m--- Epsilon_SVR regression algorithm (5) Main_SVR_Nu.m--- Nu_SVR regression algorithm
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Size: 81920 |
Author: 李俊峰 |
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Description: 该工具箱包括了二种分类,二种回归,以及一种一类支持向量机算法
(1) Main_SVC_C.m --- C_SVC二类分类算法
(2) Main_SVC_Nu.m --- Nu_SVC二类分类算法
(3) Main_SVM_One_Class.m --- One-Class支持向量机
(4) Main_SVR_Epsilon.m --- Epsilon_SVR回归算法
(5) Main_SVR_Nu.m --- Nu_SVR回归算法-Support Vector Machine Matlab Toolbox 1.0
Platform : Matlab6.5/Matlab7.0
Reference : Chih-Chung Chang, Chih-Jen Lin. "LIBSVM: a Library for Support Vector Machines"
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Size: 11264 |
Author: scott |
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Description: 这是关于svm的java源代码，带训练集，和测试集-This is about svm java source code, with training set and test set
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Size: 3257344 |
Author: fcj |
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Description: 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;
Test set: testset (); take bedroom (6:10,:) and forse (6:10,:) as the test set;
The tag set: label (); the data from the bedroom is 1 of the positive class label; the data of the forse is a negative class label of -1.)
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Size: 562176 |
Author: WanderKing
|
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Description: 利用三次二分类SVM实现三分类SVM，可以用自己的数据，完美运行。(Using the three-category SVM to implement the three-class SVM, you can use your own data to run perfectly.)
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Size: 5120 |
Author: Leo00000000 |
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