Description: SVM的matlab工具箱，可以实现支持向量基的分类和回归问题-SVM toolbox of matlab, you can realize support vector-based classification and regression problems Platform: |
Size: 150528 |
Author:张瑞 |
Hits:
Description: 用支持向量机进行分类，或者回归，进行预测控制-Using support vector machines for classification, or regression, the predictive control Platform: |
Size: 2703360 |
Author:sky |
Hits:
Description: 支持向量机的工具包,可在Matlab65下使用,具有分类和回归功能.-Support Vector Machine tool kit can be used Matlab65 with classification and regression functions. Platform: |
Size: 24576 |
Author:吴德会 |
Hits:
Description: libsvm的参数寻优程序。针对SVR提供full gridsearch方式的参数寻优，主要用于SVM回归预测-libsvm optimization process parameters. Provide full gridsearch for SVR parameter optimization approach, mainly used for SVM regression prediction Platform: |
Size: 2048 |
Author:张瑞 |
Hits:
Description: svm详细例子（包括分类，回归）等。需要的人，下载试试。多交流.我需要svm-smo的matlab源码。谁有？-SVM detailed examples (including classification, regression) and so on. Those who need it, download a try. More exchanges. Svm-smo I need the matlab source code. Who? Platform: |
Size: 375808 |
Author:2007理工信工 |
Hits:
Description: 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 LIBLINEAR include
Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
Cross validation for model selection
Probability estimates (logistic regression only)
Weights for unbalanced data
MATLAB/Octave, Java interfaces
Platform: |
Size: 521216 |
Author:陳彥霖 |
Hits:
Description: 这是一个关于最小二乘支持向量机的MATLAB仿真的例子，希望能给大家带来方便。-This is a least squares support vector machine on the MATLAB simulation examples, we hope that they will be convenient. Platform: |
Size: 31744 |
Author:蔡蓓蓓 |
Hits:
Description: 统计学习理论中提出的支撑向量机回归(SVR)遵循了结构风险最小化原则,从而避免了一味追求经验风险最小化带来的弊端-Statistical learning theory proposed by the support vector machine regression (SVR) to follow the structural risk minimization principle, thus avoiding the blind pursuit of Empirical Risk Minimization the evils of Platform: |
Size: 3072 |
Author:han fei |
Hits:
Description: 一种车牌图像校正新方法
【摘要】因摄像机角度而造成的机动车牌图像倾斜会对其后继的字符分割与识别带来不利的影响。本文在分析了车牌倾斜模式的基础上,提出了一种基于最小二乘支持向量机（LS-SVM）的车牌图像倾斜校正新方法。通过LS-SVM线性回归算法求取坐标变换矩阵并对畸变图像进行旋转校正。主要方法：首先,将二值倾斜车牌图像中的像素转换为二维坐标样本,并构造图像数据集 再通过LS-SVM线性回归算法对该数据集进行回归,求取主要参数 最后,再由该参数转换为能反映图像倾斜方向的2维坐标变换矩阵。实验结果表明,该方法简便实用,对光照、污迹等不敏感,抗干扰能力强。-New Method of a license plate image correction
Abstract caused due to camera angles, the image tilt motor vehicle license will have on its subsequent recognition of characters segmentation and adverse effects. Based on the analysis of the inclined plate model based on proposed based on least squares support vector machine (LS-SVM) of the license plate Skew new approach. By LS-SVM linear regression algorithm to strike a coordinate transformation matrix and rotate the image distortion correction. Main methods: First, the value of the two inclined plate in the image pixel is converted to two-dimensional coordinates of the sample, and construct the image data sets then the linear regression through the LS-SVM regression algorithm for the data set to strike a key parameter final , and then by the parameter is converted to reflect the tilt direction of two-dimensional image coordinate transformation matrix. The experimental results show that the method is simple and practical, to light, Platform: |
Size: 301056 |
Author:Leo |
Hits:
Description: In kernel ridge regression we have seen the final solution was not sparse in the variables ® .
We will now formulate a regression method that is sparse, i.e. it has the concept of support
vectors that determine the solution.
The thing to notice is that the sparseness arose from complementary slackness conditions
which in turn came from the fact that we had inequality constraints. In the SVM the penalty
that was paid for being on the wrong side of the support plane was given by C
P
i » k
i for
positive integers k, where » i is the orthogonal distance away from the support plane. Note
that the term jjwjj2 was there to penalize large w and hence to regularize the solution.
Importantly, there was no penalt Platform: |
Size: 51200 |
Author:bahman |
Hits:
Description: SVM方法的基本思想是：定义最优线性超平面，并把寻找最优线性超平面的算法归结为求解一个凸规划问题。进而基于Mercer核展开定理，通过非线性映射φ，把样本空间映射到一个高维乃至于无穷维的特征空间（Hilbert空间），使在特征空间中可以应用线性学习机的方法解决样本空间中的高度非线性分类和回归等问题。svm 程序，即支持向量机的代码。-The basic idea of SVM method are: the definition of the optimal linear hyperplane, and the search algorithm for optimal linear hyperplane by solving a convex programming problem. Then based on Mercer nuclear expansion theorem, through a nonlinear mapping φ, the sample space is mapped to a high-dimensional and even infinite dimensional feature space (Hilbert space), so that in the feature space can be applied to solve the linear learning machine method, the sample space The highly nonlinear classification and regression problems. svm procedures that support vector machine code. Platform: |
Size: 117760 |
Author:秀 |
Hits:
Description: 支持向量机用于对样本数据或函数的回归预测，此文件源码是用C#编写的 可以在Matlab环境下实现-Support vector machine is used to sample data or the regression function, the file C# source code is written in the Matlab environment to achieve Platform: |
Size: 81920 |
Author:lily |
Hits:
Description: 优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。-first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized Platform: |
Size: 334848 |
Author:NBB |
Hits:
Description: 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 regression can be used to do, but they are based on different theoretical basis, the return mechanism is not the same. SVM based on structural risk minimization theory, generally considered the generalization ability of neural networks than strong. To test this view, a support vector machine of this writing the general non-linear regression procedures and based on Matlab neural network toolbox of the BP neural network simulation module, the simulation results confirm that support vector machines do not only the generalization ability of non-linear regression in BP network, and neural networks to avoid the inherent shortcomings- the training results unstable. Platform: |
Size: 11264 |
Author: |
Hits:
Search - SVM regression MATLAB