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svm_v0.01beta.tar
- 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 e
lasvm-source
- 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
SVMmulticlass
- SVMmulticlass: Multi-class classification. Learns to predict one of k mutually exclusive classes. This is probably the simplest possible instance of SVMstruct and serves as a tutorial example of how to use the programmin
svm_multiclass.tar
- 用c语言写的基于SVM的多分类源码,来自康奈尔大学,性能良好-C language used SVM-based classification of multi-source, from Cornell University, the performance of good
SVM-KM
- 基于核分析的多类分类器，支持向量机的多类分类，适合研究学习，欢迎同行下载-Kernel-based analysis of the many types of classifier, support vector machine multi-category classification, suitable for study of learning, welcomed the peer download
svm_multiClass
- 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
multiboost-0.71.src.tar
- MultiBoost 是c++实现的多类adaboost酸法。与传统的adaboost算法主要解决二类分类问题不同，MultiBoost解决的是多类的分类问题，而并不是把多类转化成二类问题。-MultiBoost is a C++ implementation of the multi-class AdaBoost algorithm. AdaBoost is a powerful meta-learning algorithm com
oao
- 多分类问题的支持向量机源程序一对一方法 绝对可以运行-Multi-class SVM using One-Against-One decompositionoao
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 S
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-class-classification
- 对多类点进行分类，有图像界面，使用了多种分类算法-Points to the many types of classification, there are graphical interfaces, using a variety of classification algorithms
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 clas
PCA-feature-extraction-and-SV-multi-class
- PCA特征抽取与SVM多类分类在传感器故障诊断中的应用PCA feature extraction and SVM multi-class classification in the sensor fault diagnosis-PCA feature extraction and SVM multi-class classification in the sensor fault diagnosis
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.
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
multi-class-problem
- 将多类别问题分解成多个二类别问题是解决多类别分类问题的常用方式。传统one against all（OAA）分解方式的性能更多的依赖于个体分类器的精度，而不是它的差异性。本文介绍一种基于集成学习的适于多类问题的神经网络集成模型，其基本模块由一个OAA方式的二类别分类器和一个补充多类分类器组成。测试表明，该模型在多类问题上比其他经典集成算法有着更高的精度，并且有较少存储空间和计算时间的优势。-Decompose multi-class p
Multi-class-SVM--LS_SVMlab
- 工具箱：LS_SVMlab Classification_LS_SVMlab.m - 多类分类 Regression_LS_SVMlab.m - 函数拟合-Toolbox: LS_SVMlab Classification_LS_SVMlab.m- multi-class classification Regression_LS_SVMlab.m- function fitting
Multi-class-SVM--OSU_SVM3.00
- 一款基于svm的程序，在windows下运行，主要用于svm分类-Toolbox: OSU_SVM3.00 Classification_OSU_SVM.m- multi-class classification
Multi-Class-Video-Co-Segmentation
- 这是一篇关于多个物体类的协同分割应用到视频分割的论文。-Multi-Class Video Co-Segmentation with a Generative Multi-Video Model ；2013 IEEE Conference on Computer Vision and Pattern Recognition