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Description: 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-The toolkit includes two kinds of classification, two kinds of return, and a one-class support vector machine algorithm
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Size: 12288 |
Author: Lee Joan |
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Description: 在matlab平台下的支持向量机工具箱，已经本人测试，可用。-In the matlab platform support vector machine toolbox, I have already tested and available.
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Size: 400384 |
Author: 陈夏 |
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Description: 提出了一种基于最小二乘支持向量机的风电场风速预测方法-Proposed based on least squares support vector machines wind speed prediction method
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Author: dfiyond |
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Description: ：针对发酵过程中生物参数难以实时在线测量的问题，建立了用于生物参数状态预估的
支持向量机软测量模型。考虑到该支持向量回归(SVR)模型的复杂性和冷化特征取决于其三
个参数 ，c， 能否取到最优值，采用粒子群优化(PSO)算法实现对参数 ，c， 的同时寻优。在
此基础上，以饲料用 ．甘露聚糖酶为对象，建立了基于PSO—SVR的发酵过程产物浓度状态预估
模型。发酵罐控制结果表明：该模型具有很好的学习精度和泛化能力，可实现对 ．甘露聚糖酶
产物浓度的实时在线预估。-In view of the hardship to get real—time and on—line biological parameters in fermentation
process，a soft sensor model based on support vector machines is established for estimating the bio—
logical parameters．The complexity and generalization performance of the support vector regression
(SVR)model depend on a good setting of the three parameters ，c， ．An algorithm called parti—
cle swarm optimization(PSO)is applied to optimize the three parameters synchronously．Based on
the proposed method，a PSO—SVR model is developed to estimate the products concentration of beta—
mannanase for feedstuf．The control results of fermenter show that the state estimation model based
on PSO·-SVR has good learn ing accuracy and generalization perform ance SO as to obtain the real·-time
and on—line estimation for products concentration of beta—mannanase．
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Size: 231424 |
Author: 11 |
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Description: 模型利用协商历史中隐含的信息自动对数据进行标注以形成训练样本，用最小二乘支持向量回
归机学习此样本得到对手效用函数的估计，然后结合自己和对手的效用函数构成一个约束优化问题，用遗传算法求
解此优化问题，得到的最优解就是己方的反建议．实验结果表明，在信息保密和没有先验知识的条件下，此模型仍然
表现出较高的效率和效用-The proposed model labels the negotiation history data automatically by making full use of the implicit
information in negotiation history．Then，the labeled data become the training samples of least-squares support
vector machine that outputs the estimation of opponent’s utility function．After that，the self s utility function and
the estimation of opponent’s utility function constitute a constraint optimization problem that will be further figured
out by genetic algorithm．The optimal solution is the counter-ofer of onesel~ Experimental results show that the
proposed model is efective and efi cient in environments where information is private and the prior knowledge is
not available．
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Size: 514048 |
Author: 11 |
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Description: 为了选择神经网络的最好结构以及增强模型的推广能力，提出一种自适应支持向量回归神经网络(SVR—NN)。SVR—NN 用支持向量回归(SVR)方法获得网络的初始结构和权值， 白适应地生
成网络隐层结点，然后用基于退火过程的鲁棒学习算法更新网络结点疹教和权 主。 SVR—NN有很
好的收敛性和鲁棒性，能抑制由于数据异常和参数选择不当所导致的“过拟合，’现象。将SVR—NN
应用到时间序列预测上。结果表明，SVR．NN预测模型能精确地预测混沌时间序列，具有很好的
理论和应用价值。-Abstract：To select the‘best’structure of the neural networks and enhance the generalization ability of models．a support
vector regression neural networks fSVR-NN)was proposed．Firstly，support vector regression approach was applied to
determine initial structure and initial weights of SVR．NN SO that the number of hidden layer nodes can be constructed
adaptively based on support vectors．Furthermore，an annealing robust learning algorithm was further presented to fine
tune the hidden node parameters and weights of SVR一ⅣM The adaptive SVR．NN has faSt convergence speed and robust
capability．and it can also suppress the ‘orerfitting’phenomena when the train data ncludes outliers．The adaptive
SVR．NN was then applied to time series prediction．Experimental results show that the adaptive SVR．ⅣⅣ can accurately
predict chaotic time series，and it iS valuable in both theory and application aspects．
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Author: 11 |
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Description: 支持向量机的工具箱，自己用的，应用很方便，有使用手册，源码，中文说明。是一款很好用的软件-Support Vector Machine Toolbox, own use, the application is very accessible, user manual, source code, the Chinese statement. Is a very good software used by
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Size: 1132544 |
Author: jinzhenjun |
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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
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Size: 3072 |
Author: han fei |
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Description: 基于matlab的支持向量机应用于数据预测分析的代码-Matlab-based support vector machine applied to the analysis of the code data to predict
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Size: 1126400 |
Author: 熊伟 |
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Description: Support Vector Machine for TI DSP. Code base is for Texas Instruments CCS Environment. Highly optimized for embedded performance.
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Size: 953344 |
Author: mike_081182 |
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Description: 支持向量机(SVM)算法的源程序.包含说明材料.-Source of support vector mathine algorithm. It contains introduction paper.
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Size: 292864 |
Author: ffxzhx |
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Description: 基于SVM的股市预测程序，效果极佳，值得使用。-Support Vector Machine forecast, mainly for large-scale objects to predict.
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Size: 2048 |
Author: lees3432 |
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Description: 支持向量机matlab工具包
适用于初学者-Support vector machine matlab tool kit for beginners
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Size: 4064256 |
Author: daihong |
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Description: 用骨架法，支持向量机发对三维目标识别的最新博士论文，写的很好，也有具体的算法描述和实验结果。-With the skeleton method, support vector machines made the latest of three-dimensional target recognition doctoral dissertation, write well, but also a specific description of the algorithm and experimental results.
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Size: 18781184 |
Author: 刘玉然 |
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Description: 目标的雷达散射截面(RCS)包含了丰富的目标类别信息,如何有效利用目标RCS特征对空间目标的雷
达识别具有重要意义. 文中提取中心矩作为特征向量,采用主分量分析( PCA)进一步进行特征压缩,利用支撑矢
量-Target radar cross section (RCS) contains a wealth of objective categories of information, how the effective use of target RCS characteristics of spatial object' s radar identification of great significance. Text central moments as the feature vectors extracted, using principal component analysis (PCA) further characteristics of compression, the use of support vector
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Size: 189440 |
Author: f0700 |
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Description: 实现序贯最小优化算法，该算法可加速求解支持向量机问题-To achieve sequential minimal optimization algorithm that can accelerate the problem solving support vector machines
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Size: 4096 |
Author: 赵亮 |
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Description: 采用“纹理识别”的方式进行笔迹鉴别，利用Gabor变换提取不同频率、不同方向的笔迹特征，最后使用KNN或SVM（支持向量机）对待测样本进行类别判别。-A " texture recognition" approach to handwriting identification using Gabor transform to extract different frequency, the handwriting characteristics of different directions and finally the use of KNN or SVM (support vector machine) to treat type discriminant test samples.
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Size: 178176 |
Author: JAMES |
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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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Size: 879616 |
Author: Sheng |
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Description: SVM classification
This routine classify the training set with a support vector machine using quadratic programming algorithm (active constraints method)
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Size: 399360 |
Author: pavl |
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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,
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Size: 301056 |
Author: Leo |
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