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Description: 支持向量回归，无味卡尔曼滤波，拟合预测数据，并对含噪声的信号滤波-Support vector regression, unscented Kalman filter, fitting forecast data, and noisy signal filtering
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Size: 220160 |
Author: 李 |
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Description: 最小二乘支持向量回归算法：
训练数据用来得到回归模型，测试数据用来判断是否为显著武昌-Least squares support vector regression algorithm:
Training data to train the model, the test data for testing, to judge whether the data for gross error data
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Size: 2048 |
Author: zhu zhi |
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Description: 视频图像的人群密度检测，多种人群密度场景下人群计数算法：
算法功能:建立图像特征和图像人数的数学关系
算法输入:训练样本图像1,2…K
算法输出:模型估计参数 ，参考图像
算法流程：1)对训练样本图像进行分块处理(算法1.1)；
2)通过算法1.2，计算训练样本各个对应分块的ALBP特征归一化，再用K-means算法(可使用opencv等算法库实现，不再描述其算法)，将图像块分成k(k<K)类，获取k(k<K)个聚类中心，即为参考图像；
3)对分块的图像进行与参考图像进行匹配。使用算法1.2求取ALBP特征，并求取其相似度 ，将相似性集合作为新特征并形成一个归一化的新特征 。
4)按照行人面积占图像块面积的比例，以60 为分界，分布采用径向基核函数 和线性核函数 。K(xi,x)建立图像特征和图像人数的SVR(支持向量回归机)模型可使用opencv中的SVM或libsvm，输出模型估计参数 。
-Population density detection of video images, a variety of crowd density scenes crowd counting algorithm:
Algorithm functions: a mathematical relation between the image features and the number of images
Algorithm Input: training sample image 1,2 ... K
Algorithm output: model estimation parameters, reference image
Algorithmic process: 1) the training sample image into blocks (algorithm 1.1)
2) by 1.2 algorithm to calculate the corresponding training samples of each block ALBP features normalized, then K-means algorithm (algorithm can be used opencv library implementation, no longer describe the algorithm), the image block is divided into k (k <K) class, gets k (k <K) clustering centers, namely the reference image
3) conduct of image block matching with the reference image. 1.2 ALBP characterized using an algorithm to strike, and strike the similarity, the similarity of a set of new features and forming a normalized new features.
4) pedestrian area accounted for in
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Size: 4759552 |
Author: 徐云华 |
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Description: 全局核（线性）与局部核（高斯）的加权组合，用于改进SVM的拟合和预测能力-The global kernel (linear) and local kernel (Gauss) weighted combination, to improve the SVM ability of fitting and prediction
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Size: 2048 |
Author: 张青 |
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Description: 这个是用SMO算法做的支持向量回归程序，有测试程序-support vector regression with SMO algorithm
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Size: 4152320 |
Author: hqh |
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Description: function approximation using SVR
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Size: 2048 |
Author: masiha |
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Description: LIBSVM是台湾大学林智仁(Lin Chih-Jen)教授等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包，他不但提供了编译好的可在Windows系列系统的执行文件，还提供了源代码，方便改进、修改以及在其它操作系统上应用；该软件对SVM所涉及的参数调节相对比较少，提供了很多的默认参数，利用这些默认参数可以解决很多问题；并提供了交互检验(Cross Validation)的功能。该软件可以解决C-SVM、ν-SVM、ε-SVR和ν-SVR等问题，包括基于一对一算法的多类模式识别问题。-a simple and easy-to-use support vector machines tool for classification (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR), and distribution estimation
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Size: 633856 |
Author: 郭坦 |
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Description: SVM分类、回归预测，frauto版本，很好用-matlab SVC SVR
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Size: 830464 |
Author: 闫星星 |
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Description: 基于高斯扰动的布谷鸟算法优化svr网络结合libsvm工具包可进行回归预测-Svr network optimization toolkit can be combined with libsvm regression prediction algorithm based on Gaussian disturbance cuckoo
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Size: 2048 |
Author: 顾凯 |
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Description: 基于支持向量机的短波信号盲均衡算法，该算法稀疏性好，性能稳定。-SVM shortwave signal blind equalization algorithm based on the algorithm sparsity, and stable performance.
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Size: 12288 |
Author: zhao zhenxing |
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Description: This a weighted svr in matlab -This is a weighted svr in matlab
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Size: 2048 |
Author: Nabil |
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Description: 对LCD液晶显示器的屏幕保护程序
对LCD液晶显示器的屏幕保护程序-Screen saver for the LCD displayScreen saver for the LCD display
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Size: 20480 |
Author: bblc |
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Description: The explenation how to clustering data by using Self Organzing Map. I hope that it would be help. THanks.
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Size: 497664 |
Author: linska |
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Description: SVR trainer Matlab Code
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Size: 1024 |
Author: mohammad |
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Description: Online SVR algorithm for matlab users
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Size: 36864 |
Author: alias |
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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
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Size: 623616 |
Author: guolei |
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Description: 文献，极限学习机和SVR的结合，达到高于SVR的回归效果，且实现方便-The combination of extreme learning machine and SVR, achieve higher than that of SVR regression effect, and easy to implement
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Size: 304128 |
Author: Bruce |
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Description: 为支持向量机代码，源代码为matlab实现，可以做聚类问题-To support vector machine codes
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Size: 4143104 |
Author: 史记 |
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Description: 主要是描述在MATLAB中进行SVR超平面的推导过程，对于理解超平面的确定有很强的指导性-Mainly described in the MATLAB SVR hyperplane derivation process, to understand the determination of hyperplane has a strong guidance
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Size: 215040 |
Author: 蒋伟杰 |
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Description: epsilon-huber支持向量回归逐步推导以及详细证明，对深入理解SVR以及后续的代码编写过程有帮助。-epsilon-huber SVR gradual derivation and detailed proof of the SVR-depth understanding of the process and the subsequent coding help.
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Size: 112640 |
Author: 夏沐 |
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