Description: Support Vector Machines is a powerful methodology for solving problems in nonlinear classification and regression.
It is a matlab version.-Support Vector Machines is a powerful methamphetamine odology for solving problems in nonlinear CLAS sification and regression. It is a Matlab versi on. Platform: |
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Author:Mountain |
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Description: libsvm is a simple, easy-to-use, and efficient software for SVM
classification and regression. It solves C-SVM classification, nu-SVM
classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
regression. It also provides an automatic model selection tool for
C-SVM classification. This document explains the use of libsvm.
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Author:baolij |
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Description: SVM是一种比较新比较流行的算法，常常用在分类问题和回归问题上。
libsvm相关的matlab代码-SVM is a relatively new algorithm is more popular, often used in classification and regression issues. libsvm-related matlab code Platform: |
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Author:susan |
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Description: 基于GPU计算的SVM，VC++源码，包括详细文档说明文件。借用了GPU编程的优势，该代码据作者说比常规的libsvm等算法包的训练速度快13-73倍，预测速度快22-172倍。希望对大家有用-cuSVM is a software package for high-speed (Gaussian-kernelized) Support Vector Machine training and prediction that exploits the massively parallel processing power of Graphics Processors (GPUs). cuSVM is written in NVIDIA s CUDA C-language GPU programming environment, includes implementations of both classification and regression, and performs SVM training (prediction) at 13-73 (22-172) times the rate of state of the art CPU software. Moreover, cuSVM features a Matlab MEX wrapper so that users can access the GPU s power without having to do any "real" programming.
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Author:Sheng |
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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: 用于svc分类， 用SMO实现SVC，并在UCI数据集Iris上进行实验,2. 可借鉴现成SVM软件包，实现回归分析。
-For svc classification, using SMO to achieve SVC, and in the UCI Iris data set on the experiment, 2. SVM can learn ready-made packages, to achieve regression analysis. Platform: |
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Author:陈德志 |
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Description: 主要叙述在matlab环境下熟悉对svm的操作以及svm的主要特性包括分类和回归-The main narrative in the matlab environment familiar with the operation of the SVM and the main features include SVM classification and regression Platform: |
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Author:追风少年 |
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Description: 支持向量机的matlab商业软件包，附有中文和英文使用手册说明，可以在对算法不了解的基础上，利用简单的命令完成支持向量机的分类和回归建模-Support vector machine (SVM) commercial software package in matlab, accompanied by Chinese and English manual, can be on the basis of the algorithm does not understand, using simple commands to complete the support vector machine (SVM) classification and regression modeling Platform: |
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Author:zhuenyv |
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Description: 支持向量机的使用方法和分类回归问题在matlab环境下的实现-SVM classification and regression problems using methods and in the realization of the matlab environment Platform: |
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Author:baijie |
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Description: 分类与回归。Matlab的工具箱是围绕一个快速LS-SVM训练和模拟算法。该相应的功能调用可以用于分类以及为函数估计。该功能plotlssvm显示模型的模拟结果，在训练的区域点。-The Matlab toolbox is built around a fast LS-SVM training and simulation algorithm. The
corresponding function calls can be used for classification as well as for function estimation. The
function plotlssvm displays the simulation results of the model in the region of the training
points. Platform: |
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Author:yankee |
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Description: 支持向量机的源代码，可以实现分类和回归分析。-ctions on Intelligent Systems and Technology
ABSTRACT LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
we show formulations used in LIBSVM: C-support vector classifica-tion (C-SVC), ν-support vector classification (ν-SVC), distribution estimation (one-class SVM), -support vector regression (-SVR), and ν-support vector regression
(ν-SVR). We discuss the implementation of solving quadratic problems Platform: |
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Author:guolei |
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Description: 采用波束成形技术的BER计算，zcZuvVR参数可以实现模式识别领域的数据的分类及回归，是机器学习的例程，在MATLAB中求图像纹理特征，dvGzQFM条件实现了对10个数字音的识别，包括最小二乘法、SVM、神经网络、1_k近邻法。- By applying the beam forming technology of BER zcZuvVR parameter You can achieve data classification and regression pattern recognition, Machine learning routines, In the MATLAB image texture feature, dvGzQFM condition To achieve the recognition of 10 digital sound, Including the least squares method, the SVM, neural networks, 1 _k neighbor method. Platform: |
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Author:hihfzk |
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Description: SVM(Support Vector Machine)指的是支持向量机，是常见的一种判别方法。在机器学习领域，是一个有监督的学习模型，通常用来进行模式识别、分类以及回归分析。
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。
支持向量机方法是在后来提出的一种新方法。
SVM的主要思想可以概括为两点:
它是针对线性可分情况进行分析，对于线性不可分的情况，通过使用非线性映射算法将低维输入空间线性不可分的样本转化为高维特征空间使其线性可分，从而使得高维特征空间采用线性算法对样本的非线性特征进行线性分析成为可能。
它基于结构风险最小化理论之上在特征空间中构建最优超平面，使得学习器得到全局最优化，并且在整个样本空间的期望以某个概率满足一定上界。(442/5000
SVM(Support Vector Machine) refers to Support Vector Machine, which is a common discriminant method. In the field of machine learning, it is a supervised learning model, which is usually used for pattern recognition, classification and regression analysis.
Vapnik et al. proposed another design criterion for linear classifier on the basis of years of statistical learning theory. The principle is also derived from linear separability, and then extends to linear inseparability. Even extending to the use of nonlinear functions, this classifier is called Support Vector Machine (SVM). The support vector machine has a deep theoretical background.) Platform: |
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Author:飛上云端 |
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Description: SVM(Support Vector Machine)指的是支持向量机，是常见的一种判别方法。在机器学习领域，是一个有监督的学习模型，通常用来进行模式识别、分类以及回归分析。(SVM (Support Vector Machine) refers to support vector machines, which is a common method of discrimination. In the field of machine learning, it is a supervised learning model, which is usually used for pattern recognition, classification and regression analysis.) Platform: |
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Author:束负禾火 |
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