搜索资源列表
LS-SVM
- 这是《数据挖掘中的新方法——支持向量机》一书的电子版。
MYGASVM
- simple GASVM for LS-SVM
LS-SVMlab1.5
- 一个国外大学开发的SVM工具包,有英文的使用说明。
CSRC_windows_LS-SVMlab1.5.tar
- 不错的SVM实现算法,采用的是LS-SVM算法,这是C版本,还有一个MATLAB版本-good SVM algorithm, using the LS-SVM, which is C version, a version of MATLAB
LS_SVM最小二乘支持向量机Matlab源码
- 自编的最小二乘支持向量机Matlab代码,主要用于非线性回归
LS—SVM
- 最小二乘支持向量机
arima.rar
- 在matlab的环境下实现了自回归移动平均模型(arima),Matlab environment in the realization of the auto-regressive moving average model (arima)
lssvmpso.rar
- 智能微粒群为最小二乘支持向量机调参的示例程序(LSSVM+PSO),Intelligent Particle Swarm and least squares support vector machine modeling(LSSVM+ PSO)
GA
- 在matlab平台下,用GA对的lssvm的参数进行优化,很有用的东西。-Platform in matlab, using the GA to optimize parameters lssvm, very useful things.
LSSVMlabv1_8_R2009b_R2011a
- LS-SVM toolbox 里面集合了所有的最小二乘法支持向量机的工具函数,可以直接调用。-LS-SVM toolbox里面集合了所有的最小二乘法支持向量机的工具函数,可以直接调用。
libsvm-mat-2.88-1
- 最小二乘支持向量机工具包,内嵌在Matlab环境中运行-LS—SVM tools
81404570LS-SVMlab1.5aw
- 详细讲解了支持向量机的原理及其实现过程,能够很好地理解支持向量机-Explained in detail the principle of support vector machines and its implementation process, can be a good understanding of support vector machines
CSRC_linux386_LS-SVMlab1.5.tar
- linux下matlab可用的最小二乘法支持向量机ls-svm,c语音代码。-matlab under linux available method of least squares support vector machine ls-svm, c voice code.
sdarticle18
- A differential pulse stripping method for the simultaneous determination of lead and tin is proposed. The procedure involves accumulation with dissolving of lead and tin on a hanging mercury drop electrode (HMDE), followed by oxidation of dissolv
ls-svm
- 最小二乘支持向量机Support Vector Machines-Support Vector Machines
seqsvmMATLAB-source-code
- LS-SVM最小二乘支持向量机的matlab工具箱-LS-SVM support vector machine matlab toolbox
paper5
- The paper title is: License Plate Recognition System Based on Morphology and LS-SVM
LS-SVMLab-v1.7(R2006a-R2009a)
- 支持向量机SVM(Support Vector Machine)它在解决小样本、非线性及高维模式识别中表现出许多特有的优势,并能够推广应用到函数拟合等其他机器学习问题中-Support Vector Machine SVM (Support Vector Machine) it addresses the small sample, nonlinear and high dimensional pattern recognition performance of many unique adva