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SVMClassification
- 这是我自己的SVM分类程序，本人正在用包括核函数的选择，线性核函数等。-This is my own SVM classification procedures, I was with, including the choice of kernel function, linear kernel and so on.
Scene-Classification
- 提供了三类场景“bedroom”、“CALsuburb”、“industrial”的样本特征集以及原始图像，分别用线性分类器、树状分类器、SVM分类器以及AdaBoost分类器对其进行区分。其中AdaBoost分类器有部分内容调用了Vezhnevets Alexander编写的源码-Provides three types of scenes " bedroom" , " CALsuburb" , " industrial" sample fea
CSL1.0
- A Comprehensive Sparse Learning Package-Descr iptions: Six sparse algorithms are implemented in this package. They are 1) SVM 2) L1-norm SVM a) Linear Programming SVM b) SC-SVM 3) L0-norm SVM 4) RVM (Relevance Vector Machine) 5)
SVM_GUI_3.1[mcode]{by-faruto}
- 支持向量机SVM(Support Vector Machine）作为一种可训练的机器学习方法，依靠小样本学习后的模型参数进行导航星提取，可以得到分布均匀且恒星数量大为减少的导航星表。 基本情况 Vapnik等人在多年研究统计学习理论基础上对线性分类器提出了另一种设计最佳准则。其原理也从线性可分说起，然后扩展到线性不可分的情况。甚至扩展到使用非线性函数中去，这种分类器被称为支持向量机（Support Vector Machine，简称SVM）。支持向量机的提出有很深的理论背景。 支持向量机
Pattern-Recognition2
- 清华模式识别第二次作业，采用dataset2.txt 数据作训练样本，采用身高与体重特征进行性别分类，建立最小错误贝叶斯分类器；2、采用身高体重数据作为特征，以 dataset2.txt 作为训练数据，用 Fisher 线性判别方法设计分类器；3、从多层感知器、SVM、近邻法选择一种方法，进行上述的分类实验；-Tsinghua second operation pattern recognition using dataset2.txt data for training samples, us
lec3
- The Support Ve ct or Machi ne So fa r we have used a reference as sumpt ion tha t there exists a linear classifier that has a larg e ge ometric margin, i.e., whose decision b oundar y is we ll separa ted from all the training images (e xampl
SVM_matlab
- 利用SVM做回归线性测试。对于大盘指数的有效预测可以从整体上观测股市的变化提供强有力的信息，所以对上证指数的预测很有意义。通过对上证指数从1990.12.20-2009.08.19每日的开盘数进行回归分析，最终拟合的结果是：均方误差MSE=2.35705 e-005，平方相关系数 R=99.9195 。SVM的拟合结果还是比较理想的。-Make use of SVM linear regression testing. For the market index can predict the o
svm_-linear_unseparable
- opencv实现的svm，对标准svm原理做了基本的改进，能够处理线性不可分的情况。-opencv realized svm, svm standard basic principle made improvements to handle the case of non-linear.
svcerrorfunction
- In the case where a linear boundary is inappropriate the SVM can map the input vector for error
svcplot
- In the case where a linear boundary is inappropriate the SVM can map the input vector for plotting
svmkernel
- In the case where a linear boundary is inappropriate the SVM can map the input vector for kernel
svcnobias
- In the case where a linear boundary is inappropriate the SVM can map the input vector for relibility noibas
linear-programming-and-svm
- 线性分类程序，支持向量机的matlab实现-liner programming svn matlab implementation!
multi-kernels-SVR
- 全局核（线性）与局部核（高斯）的加权组合，用于改进SVM的拟合和预测能力-The global kernel (linear) and local kernel (Gauss) weighted combination, to improve the SVM ability of fitting and prediction
libsvm-3.1
- 台湾林智仁教授开发的svm开发包 内含java matlab c++等多种语言的开发模块-SVM no-linear
SVM_example
- SVM支持向量机一个简单应用，将二维平面内的两类点（空心圆点和空心方点）进行线性分类，并对分类后的区域进行不同颜色的填充。-SVM support vector machine with a simple application, the two types of point (hollow dots and hollow square dots) within the two-dimensional linear classification, and area classification
svm1
- Linear support vector machine separates two classes of data.the basic idea behind SVM is to create a hyperplane that optimally separates the two classes of data.
SVMTutorial
- In this tutorial we present a brief introduction to SVM, and we discuss about SVM published papers, workshop materials & material collected books and material available online on the World Wide Web. In the beginning we try to define SVM and try to ta
pkupr
- 模式识别 北京大学 本科生课程 课件 （包括贝叶斯模型、最近邻、SVM、线性与非线性分类器、boosting、统计学习、非监督学习等）-Pattern Recognition Peking University Courseware (including Bayesian model, the nearest neighbor, SVM, linear and non-linear classifiers, boosting, statistical learning, unsupervised
static_SVM_line_mean_var
- MATLAB代码，利用支持向量机SVM，核函数为线性核函数并进行参数寻优对数据进行分类-MATLAB code, the use of support vector machine SVM, kernel function is linear kernel parameter optimization and data classification