搜索资源列表
libsvm3
- 台湾林智仁编写的支持向量机开源程序,可用于分类(C-SVC,nu-SVC,one-class SVM)和回归(epsilon-SVR,nu-SVR)。这是最新版本3.0。-Libsvm3.0 is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-cla
fourSVMtool
- 四种支持向量机的四种SVM工具箱的分类与回归算法,包括全部工具箱函数。-Four of four SVM support vector machine classification and regression algorithm toolbox, including all the toolbox functions.
wekajava
- 由java开发的软件包,里面有人工智能所用的很多东东,包括神经网络,支持向量机,决策树等分类和回归分析方法,集成化软件哦-by java development package, which has artificial intelligence used by many of the Eastern, including neural networks, support vector machines, such as decision tree classification and regr
TorchSVM
- SVM(支持向量机)的C++源码。可以实现基于SVM的数据分类机回归,可应用于人工智能,模式识别及数据处理领域。源码附有英文注释。-SVM (support vector machine) in C++ source code. Can achieve data classification based on SVM regression, can be used in artificial intelligence, pattern recognition and data processin
MATLABS-SVC--CODE
- 支持向量机的MATLAB代码,包括分类与回归,可帮助学习机器学习算法-MATLAB S SVC DODE
OnlineSVR_Matlab_7.0_Code
- 在线支持向量机回归算法的matlab实现-Online support vector machine regression algorithm matlab implementation
SVM-REGRESSION
- 一篇关于支持向量机回归算法的研究与应用,较详细的介绍了有关支持向量机的原理和使用方法。-intrudce the principle of SVM regression and the method of using it.
svm_matlab_gui
- 支持向量机matlab工具箱(含资料及gui模式)用于分类和回归预测-SVM matlab toolbox (including information and gui mode) for classification and regression
Supportvm.regression
- matlab编的一个支持向量机的数据分类和回归分析程序,很不错-matlab code of a support vector machine classification and regression data analysis program, very good
MATLAB_SVM_Toolbox
- 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-Kit includes two kinds
SVM
- 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
Matlab-svm-BP-compare
- 支持向量机和BP神经网络虽然都可以用来做非线性回归,但它们所基于的理论基础不同,回归的机理也不相同。支持向量机基于结构风险最小化理论,普遍认为其泛化能力要比神经网络的强。为了验证这种观点,本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块,仿真结果证实,支持向量机做非线性回归不仅泛化能力强于BP网络,而且能避免神经网络的固有缺陷——训练结果不稳定。-SVM and BP neural networks, although non-linear regr
SVM_classandregress
- 支持向量机 内容中主要包括二种分类,二种回归,以及一种一类支持向量机算法。-Support Vector Machine Two types of categories, and two types of regression, as well as a kind of support vector machine algorithm is included in this content.
mySVM_C
- c语言开发的支持向量机,用于分类和回归。-Support vector machine (SVM) based on C language
svm-chinese
- 非线形回归,支持向量机理论。详细地介绍了支持向量机的数据基础。举例说明支持向量机的应用。-Non-linear regression, support vector machine theory. Detailed descr iption of the support vector machine data base. Illustrate the application of support vector machines.
adaptive-genetic-algorithm
- 自适应GA SVM 参数选择算法研究Param eter selection algorithm for support vector machines based on adaptive genetic algorithm 支持向量机是一种非常有前景的学习机器, 它的回归算法已经成功地用于解决非线性函数的逼近问题. 但 是, SVM 参数的选择大多数是凭经验选取, 这种方法依赖于使用者的水平, 这样不仅不能获得最佳的函数逼近效果, 而且采用人工的方法选择 SVM 参数比较浪费
support-vector-machine
- 本书主要以分类问题(模式识别,判别分析)和回归问题为背景,系统阐述支持向量机和相应的优化算法-This book mainly classification (pattern recognition, discriminant analysis) and regression as the background, the system describes support vector machine and the corresponding optimization
LibSvm
- 支持向量机的方法在matlAB中实现,包括回归、以及分类等模块。此外,还有参数寻优的算法。-Support vector machine method is implemented in matlAB, including regression and classification module. In addition, parameter optimization algorithms
svm3
- 使用支持向量机进行非线性回归,得到非线性函数y=f(x1,x2,…,xn)的支持向量解析式,求解二次规划时调用了优化工具箱的quadprog函数。本函数在程序入口处对数据进行了[-1,1]的归一化处理,所以计算得到的回归解析式的系数是针对归一化数据的,仿真测-Using support vector machines non-linear regression
matlab-svm1
- 基于回归分析的支持向量机计算程序算例及m文件-Support vector machine application example and m files On the basis of regression analysis