图像压缩算法-SPIHT的MATLAB实现

finite difference method with Crank-Nikolson approach(SOR) for American call option

对比粒子群算法中不同的权重因子C1C2对算法收敛的影响(different C1、C2 to particle swarm optimization)

AdaBoost元算法属于boosting系统融合方法中最流行的一种，说白了就是一种串行训练并且最后加权累加的系统融合方法。 具体的流程是：每一个训练样例都赋予相同的权重，并且权重满足归一化，经过第一个分类器分类之后， 计算第一个分类器的权重alpha值，并且更新每一个训练样例的权重，然后再进行第二个分类器的训练，相同的方法....... 直到错误率为0或者达到指定的训练轮数，其中最后预测的标签计算是各系统*alpha的加权和，然后sign（预测值）。 可以看出，训练流程是串行的，并且训练样例的权重是一直在变化的，分错的样本的权重不断加大，正确的样本的权重不断减小。 AdaBoost元算法是boosting中流行的一种，还有其他的系统融合的方法，比如bagging方法以及随机森林。 对于非均衡样本的处理，一般可以通过欠抽样(undersampling)或者过抽样(oversampling)，欠抽样是削减样本的数目， 过抽样是重复的选取某些样本，最好的方法是两种进行结合的方法。 同时可以通过删除离决策边界比较远的样例。 (AdaBoost boosting systems dollar fusion algorithm is the most popular one, it plainly systems integration approach is a serial train and final weighted cumulative. Specific process is: Each training example is given equal weight, and the weights satisfy normalization, after the first classifiers after Calculating a first classifier weights alpha value for each sample and updates right weight training, and then the second classifier training, the same way ....... 0, or until the specified error rate training rounds, wherein the label is the calculation of the final prediction system* alpha weighted and then sign (predicted value). As can be seen, the training process is serial, and weight training examples is always changing, the right of the wrong sample weight continued to increase, the right to correct sample weight decreasing. AdaBoost algorithm is an element, as well as other methods of boosting popular systems integration, such as bagging and random forest method. For )

圆形阵波束形成仿真，包括声压噪声的和矢量圆形阵的仿真图(Simulation of circular array beamforming, including sound pressure noise circular array and vector map of the simulation)

说明：  卡尔曼滤波实验和卡尔曼时间预测 包括从gps数据中读取经纬度(Time Kalman filter and Kalman prediction experiments, including data read from the gps latitude and longitude)

利用matlab软件仿真斜条纹和竖条纹的干涉图样(Software simulation using matlab interference pattern diagonal stripes)

多元测试信息融合课程的编程作业，利用DS理论进行数据融合，有相关的例题(Multivariate testing information fusion programming assignments courses use DS theory for data fusion, there are relevant examples)

A Matlab implementation of the Jonker-Volgenant algorithm solving LAPs

matlab教程，包括初步、启动、创建矩阵、对象、画图、拟合、线性方程等。(matlab tutorials, including preliminary, start, create a matrix, object, drawing, fitting, linear equations and so on.)

COntrole de courant avec Modulation Vectorielle