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stprtool.rar
- 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines,
gmm
- 高斯混合模型的详细实现 内有多个.m文件-Gaussian mixture model has a number of detailed implementation. M files
cuSVMVCcode
- 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 proc
libsvm
- 基于matlab的SVM（支持向量机）算法。作为非常流行的svm工具，可以实现基于SVM的数据分析，能够应用于人工智能及模式识别领域。-Matlab based on the expectation-maximization algorithm for Gaussian mixture model (GMM) toolkit. GMM-based data can be analyzed, can be used in the field of artificial intelligence a
SVM
- SVM开发包，接口明了，将它的路径添加到当前工程即可直接调用 接口举例如下 Ytest_try=[] 测试样本的特征 Ytest_try=rightsidetest(Xtest_try,index) ypred=[] 训练样本的标注 Ytest=normo(Ytest) kernel= gaussian kerneloption=1 ypred = svmval(Ytest_try,xsup_ch,w_ch,w0_
SVM-2D-Classification-
- SVM 2D Classification Comparing Gaussian and polynomial Kernels -SVM 2D Classification Comparing Gaussian and polynomial Kernels
7-djvu-9812381511
- This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpret
lubanglssvm
- 基于鲁棒学习的最小二乘支持向量机及其应用 鉴于最小二乘支持向量机比标准支持向量机具有更高的计算效率和拟合精度, 但缺少标准支持向量机的鲁 棒性, 即当采样数据存在奇异点或者误差变量的高斯分布假设不成立时, 会导致不稳健的估计结果, 提出了一种鲁棒 最小二乘支持向量机方法. 该方法在最小二乘支持向量机基础上, 通过引入鲁棒学习方法来获得鲁棒估计. 仿真分析 及某湿法冶金厂的应用实例验证了该方法的可行性和有效性.- Least squares support vector mac
Digital-iamge_MFC
- 运用C实现了数字图像的一些基本算法，包括canny edge detection,阈值变换，维纳滤波，直方图均衡，图像细化，旋转，图像配准，图像分割kmeans, isodata, fuzzy c means, fuzzy clustering, mrf image seg, 图像浏览，road svm using matlab, snakes matlab, anistropic gaussian filter-Canny edge detection, threshold transform
locv
- 最先进的KPCA主成分提取法，加最先进的高斯SVM法，再加传统的交叉验证学习预测法。-The most advanced KPCA principal components extraction method, and the most advanced gaussian SVM method, then add the traditional cross validation forecast method of learning.
classifer
- 二分类问题采用包括逻辑回归、最小二乘法、感知器算法（按下space不断迭代）、svm线性分类，另外还有高斯分线性分类（待完善），针对平面上两类点进行分类-Second classification using logistic regression, the method of least squares, perception algorithm (Press space iteratively) svm linear classification, in addition to the Ga
classification
- 机器学习中几种典型的分类算法，SVM, ML, Gaussian Mixture Model等-typical classifiers(SVM, ML) in ,machine learning.
svm
- Gaussian support vector binary classifier
svm-demo
- 一个svm的演示程序，能演示两类数据分类，有gui界面，不使用第三方工具箱，使用gaussian核函数，界面能设置c和gamma的参数值，最后可以得到分类情况的可视化效果。针对svm算法的研究者和用于教学演示的教师，是个不错的源码。-An svm demo program that can demonstrate two types of data classification, gui interface, do not use third-party toolbox, using gauss
svm
- 对支持向量机硬间隔，软间隔和核函数的实现，采用的核为高斯核，高斯核所需的数据集包含在data中。-Support vector machines for hard intervals, soft interval and kernel implementations, using Gaussian kernel by kernel, Gaussian kernel required data set contains the data in.
Genetic-algorithm-to-optimize-svm
- 遗传算法优化支持向量机的惩罚系数C与高斯核系数g，能够提高支持向量机的分类精度-Genetic algorithm to optimize the punish coefficient of support vector machine (SVM) with gaussian kernel coefficient C g, can improve the support vector machine (SVM) classification accuracy
GA-PSO
- 遗传算法和粒子群算法以及网格搜索法优化神经网络SVM的高斯核参数和惩罚参数-Optimization of Genetic Algorithm Neural Network SVM Gaussian kernel parameters and penalty parameter
influence-of-parameter-in-SVM
- 验证SVM中惩罚系数C与高斯核宽度系数g对于其分类性能的影响-Test the penalty parameter C and the the gaussian kernel factor g for classification influence of SVM
SVM
- 支持向量机分类程序，使用高斯核函数，SMO顺序最优化算法，为学习SVM提供参考-SVM program, using a Gaussian kernel, SMO sequence optimization algorithm to provide a reference for learning SVM
S1IM159.【已完成】基于SVM的烟雾识别系统
- 基于SVM的烟雾识别系统 运动区域提取使用的是 自适应混合高斯背景建模 特征算法使用的是 HOG+LBP 机器学习方法使用的是 scikit的SVM(Smoke recognition system based on SVM The moving region extraction uses adaptive Gaussian mixture background modeling feature algorithm, hog + LBP machine learning method