Description: 统计模式识别、线性或非线性回归以及人工神经网络等方法是数据挖掘的有效工具，支持向量分类（support vector classification，简称SVC）算法是一个很有发展前景的方向。-Statistical pattern recognition, linear or nonlinear regression and artificial neural network approach is an effective tool for data mining, support vector classification (support vector classification, referred to as SVC) algorithm is a promising direction. Platform: |
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Author:xs |
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Description: SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
-SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
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Author:jon |
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Description: SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
-SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
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Author:jon |
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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。
-Support Vector Machine and BP neural network, even though there can be used to make non-linear regression, but they are based on the theoretical basis for the different, the mechanism of regression is not the same. Support vector machine based on structural risk minimization theory, generally considered the generalization ability of neural networks than the strong. To test this view, the paper prepared by non-linear regression support vector machine procedures and based on a common Matlab neural network toolbox of BP neural network Platform: |
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Author:孙准 |
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Description: 支持向量机非线性回归通用matlab程序，本程序使用支持向量机法，实现对数据的非线性回归，核函数的设定和修改在函数内部进行，数据预处理在函数外部进行，简单易懂，希望能对大家有所帮助-Universal non-linear regression support vector machine matlab program, this program uses support vector machine method to achieve non-linear regression of data, settings and modify the kernel function within the function, the data pre-processing function externally, simple and easy to to understand, hoping to be helpful to everyone Platform: |
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Author:韩锡辉 |
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Description: 支持向量机非线性回归通用MATLAB源码本源码可以用于线性回归、非线性回归、非线性函数拟合、数据建模、预测、分类等多种应用场合-Universal non-linear regression support vector machine MATLAB source code of this source can be used for linear regression, nonlinear regression, nonlinear function approximation, data modeling, prediction, classification and other applications Platform: |
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Author:fangcao |
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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: |
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Author:Leo |
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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: |
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Author:秀 |
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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: |
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Description: 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 description of the support vector machine data base. Illustrate the application of support vector machines. Platform: |
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Author:Lancer |
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Description: K-近邻支持向量机回归，工具箱，全程Matlab-K-Support Vector machine Matlab， This function process the SVM regression model using a linear epsilon insensitive cost Platform: |
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Author:wjs |
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Description: matlab中的标准svm程序源码，用于解决线性的回归问题，不能用于解决非线性，区别于最小二乘支持向量机。-svm program source code, standard Matlab is used to solve linear regression problems, can not be used to solve nonlinear, different from the least squares support vector machine. Platform: |
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Author:王欣 |
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Description: 支持向量机回归理论与神经网络等非线性回归理论相比具有许多独特的优点有线性回归和非线性回归,其模型的选 择包括核的选择、容量控制以及损失函数的选择.在控制方面的研究包括非线性 时间序列 的预测及应用、系统辨识以及优化控制和学习控制等方面的研究-Support vector machine (SVM) regression theory and neural network has many unique advantages such as nonlinear regression theory with linear regression and nonlinear regression, the choice of its model including the selection of nuclear, volume control, and the choice of loss function. In the control of nonlinear time series prediction and application, including system identification and optimization control and learning control research Platform: |
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Author:mumu |
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Description: 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
试需使用与本函数配套的Regression函数。- Using non-linear support vector machine regression, nonlinear function y = f (x1, x2, ..., xn) support vector analytic, Optimization Toolbox quadprog call function when solving quadratic programming. The function of the data in the program carried out at the entrance [-1,1] of normalization, so the regression coefficients calculated analytical formula is for the normalization of data, simulation test test need to use this function supporting The Regression functions. Platform: |
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Author:luban |
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Description: 针对各种机器学习，深度学习领域的一个matlab工具包-A machine learning library focused on deep learning.Following algorithms and models are provided along with some static utility classes:
- Naive Bayes, Linear Regression, Logistic Regression, Softmax Regression, Linear Support Vector Machine, Non-Linear Support Vector Machine (with RBF kernel),
Feed-forward Neural Network, Embedding Neural Network, Convolutional Neural Network, Sparse Autoencoders, Denoising Autoencoders,
Contractive Autoencoders, Stacked Sparse Autoencoders, Self-Taught Learner and Restricted Boltzmann Machines are tested with this version.
- Rest of the methods are not tested hence not supplied and the progress is as follows:
+ Deep Belief Nets with Restricted Boltzmann Machines (not tested)
+ Bayes Nets (tested- refactoring)
+ Hidden Markov Models (tested- refactoring)
+ Conditional Random Fields (work in progress) Platform: |
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Author:zhjhe |
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Search - linear support vector regression