matlab_PDFTOOL

By using these matlab codes one can generate any noise with mean X and variance Y . Useful for analysis of signals in noisy environment

基于小波包的图像去噪的matlab源码 请多指教(Denoising based on wavelet packet source matlab please enlighten)

二维贝叶斯反演方法的实现matlab代码，包含地质统计学知识(The realization of two-dimensional Bayesian inversion method matlab code, including geostatistics knowledge)

这是一个无线网络中的信道仿真程序，可供通信系统建模的朋友参考(This is the wireless channel model program)

TabActivity实现多页面切换,经典的底部导航栏功能(TabActivity demo)

本程序为扩展卡尔曼滤波(ekf)的程序代码，可以供初学者学习。(This procedure is extended Kalman filter (ekf) code, for beginners to learn.)

《MATLABSimulink通信系统建模与仿真实例精讲》DS_CDMA通信系统(" MATLABSimulink communications system modeling and simulation examples succinctly" DS_CDMA Communication Systems)

这是一个低通抽样定理，例题中包括了低通信号的波形，抽样速率为4Hz的抽样信号，抽样序列恢复出原始信号(This is a low-pass sampling theorem, examples included in the lowpass signal waveform, the sampling rate is 4Hz sampling signal to recover the original signal sample sequence)

Data Mining methods in Classification

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)