economics

这里实现了基于四种SVM工具箱的分类与回归算法: 1、工具箱：LS_SVMlab Classification_LS_SVMlab.m - 多类分类 Regression_LS_SVMlab.m - 函数拟合 2、工具箱：OSU_SVM3.00 Classification_OSU_SVM.m - 多类分类 3、工具箱：stprtoolsvm Classification_stprtool.m - 多类分类 4、工具箱：SVM_SteveGunn Classification_SVM_SteveGunn.m - 二类分类 Regression_SVM_SteveGunn.m - 函数拟合 更详细的相关函数说明请通过help命令查看！(Here the realization of the four SVM toolbox based on the classification and regression algorithm: 1, Toolbox: LS_SVMlabClassification_LS_SVMlab.m- Multiclass Classification Regression_LS_SVMlab.m- function fitting 2, the toolbox: OSU_SVM3.00Classification_OSU_SVM.m- Multiclass Classification 3, Toolbox: stprtoolsvmClassification_stprtool.m- Multiclass Classification 4 toolbox: SVM_SteveGunnClassification_SVM_SteveGunn.m- II Category Regression_SVM_SteveGunn.m- function fitting a more detailed explanation of the correlation function through the help command to view!)

考虑两个参数的一维三角概率模型,用贝叶斯方法对其进行估计，了解贝叶斯估计方法.(Consider the two parameters of one-dimensional triangular probability model, using Bayesian methods to estimate their understand Bayesian methods.)

说明：  一个matlab关于同步电机的仿真，很有价值的(A matlab simulation on the synchronous motor, great value)

说明：  数字图象处理,显示图象性质并且将索引图形转化为二值话图象(digital image processing and display images of nature and graphics index values into two words image)

通过输入区间端点a和b以及精度，初始等分数和最大外推层数来近似f(x)在a到b上的积分！(By entering a and b as well as the interval endpoint accuracy, the initial fraction and the like to approximate the maximum number of layers extrapolation f (x) at the points a to the b!)

DUAL TREE MATLAB CODE

用于初学者在matlab中绘制魔方，初学者可以下载学习其中步骤(magic cube in matlab)

TD-LTE PMCH MATLAB source code

用有限差分法、多项式拟合方法和三次样条插值方法对离散数据进行数值微分(Using the finite difference method, polynomial fitting method and cubic spline interpolation method for numerical differentiation of discrete data)

采用身高和体重数据作为特征，分别假设二者相关或不相关，在正态分布假设下估计概率密度，建立最小错误率Bayes分类器，写出得到的决策规则，将该分类器应用到训练/测试样本，考察训练/测试错误情况。(The use of height and weight data as the characteristics of the assumption that the two were related or not related to the normal distribution probability density is estimated under the assumption that the establishment of Bayes minimum error rate classifier, the decision-making rules have been written, the classifier is applied to training/test samples to study the training/testing error conditions.)