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数学建模2001年A题论文，VC++程序和Matlab程序处理100张血管的三维重建(A problem of mathematical modeling paper in 2001)

神经网络,数字识别,水印程序,matlab 里面有关于matlab数字识别的多份代码及资料， (Neural network, digital identification, watermark program, matlab inside matlab figures on the number of identification code and information)

matlab的高阶谱工具箱 内有说明 有兴趣的朋友可以参考(matlab toolbox containing high-level spectrum shows friends who are interested can refer to the)

matlab program for random noise addition

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. (This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. )

Sparse recovery algorithms from exact and incomplete data

因子分析求得共同度，因子贡献，因子贡献率，得分函数的系数(Factor analysis to achieve common degree, factor contribution coefficient factor contribution rate, scoring function)

决策树,包括决策树的节点选择、决策控制及决策树的画法(decision tree)

基于小波变换模极大值用于检测信号突变点，非原创，测试可用(Based on wavelet transform modulus maxima for point mutation detection signal, non-original, test available)

GUASS 基本消元法及列选主元素消元法Matlab 算法程序实现(Gaussian elimination method)