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libsvm-2.88.rar
- 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 Univ
stprtool.rar
- 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,
SVM-classifier
- 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.
libsvm-mat-2[1].89-3
- svm多分类器，包括多分类和GA算法和PSO算法优化的SVM-svm multi-classifier, including the multi-classification and GA algorithm and PSO algorithm for optimization of SVM
libsvm-2.89
- 是一種線性方成的分類器。SVM透過統計的方式將雜亂的資料以NN的方式分成兩類，以便處理。LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM. -Main features of LIBLINEA
demosvm
- matlab编写的svm实现多类分类的源代码，训练算法包括OAA算法、OAO 算法、BSVM2算法等。-matlab prepared svm multi-category classification of the source code, training algorithms, including OAA algorithm, OAO algorithm, BSVM2 algorithm.
Multi-classSVMClassifierUtilizingBinaryDecisionTre
- 有关svm 多类的问题，用二叉树来解决的。一篇英文。可以参考。-The svm multi-class problems, and use a binary tree to be resolved. One in English. Can refer to.
SVM
- 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 in
SPIDER_mclass
- Multi-class Coding (adapted from from LS-SVM for SPIDER). Encode (code_MOC, code_ECOC, code_OneVsAll, code_OneVsOns) and decode (codedist_hamming, codedist_bay) a multi-class classification task into multiple binary classifiers.
svm-light
- 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
multiClass_svm
- 一个svm(向量机)分类算法本质上是二类分类器代码，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。本程序源码就是一种基于svmlight的svm多类分类器实现。对分类感兴趣的用户请参照。-A svm (vector machine) classification algorithm is essentially a second-class classification code, multi-class classification methods are generally
SVM
- C++实现的SVM程序，可以进行多类分类。-C++ implementation of the SVM procedure, can be multi-class classification.
Recognition-activities-using-SVM
- 利用Support Vector Machine來處理對影像辨識，能判斷影像所傑取到的人是處於何種動作之下，最後並比較多種分類器之結果-Recognition of human activities using SVM multi-class classifi er,including used o-v-o,o-v-a,DAGSVM and SVM-BTA to compare.
credit-rating-using-multi-class-SVM
- 一個基於多類支援向量機的應用，將支向機應用在企業之信用評比上，能使企業得知自身所具有之優勢與劣勢，藉由改善不足之處來提升企業本身信用。-A corporate credit rating model using multi-class support vector machines to do more effective actions in performance
Multi-class-SVM-Image-Classification
- 基于神经网络的遥感图像分类取得了较好的效果,但存在固有的过学习、易陷入局部极小等缺点.支持向量机机器学习方法,根据结构风险最小化(SRM)原理,表现出很多优于其他传统方法的性能,本研究的基于多类支持向量机分类器的遥感图像分类取得了达95.4 的分类精度.但由于遥感图像分类类别多,所需训练样本较大,人工选择效率较低,为此提出以人工选择初始聚类质心、C均值模糊聚类算法自动标注训练样本的基于多类支持向量机的半监督式遥感图像分类方法,期望能在获得适用的分类精度的基础上有效提高分类效率-Neural ne
SVM-function-available
- 可以实现SVM函数曲线拟合，支持向量机曲线逼近，多类分类等等强大功能，无需修改源程序，直接可用-The SVM function curve fitting, support vector machines curve approximation, powerful multi-class classification, etc., without modifying the source code directly available
SVM-multiple-class
- 关于matlab的多分类问题的几篇文章，对于学习matlab 中的svm 多分类问题有帮助-Multi-classification on matlab several articles for learning in matlab svm multi-classification helpful
svm
- 于多类模式分类，使用svm工具箱：LS_SVMlab 使用平台：Matlab6.5 -Multi-class pattern classification, using svm toolbox: LS_SVMlab using the platform: Matlab6.5
SVMRFE.m
- 基于RFE特征选择方法的多分类特征排序，Matlab平台(Multi class feature ranking based on RFE method)
libsvm-mat-2[1].89-3[FarutoUltimate3.0Mcode]
- 一般的支持向量机只支持二分类，使用libsvm可以实现多分类，原理也是基于二分类，然后在使用投票机制，经测验，libsvm的分类精度可达85%以上(Multi class supported by libsvm,after testing, the classification accuracy can reach 85%.)