zhengzhidong-kongshipuguji

Vapnik-Cheervonenkis (VC) Dimension 􀂇 Support Vector Machines 􀂇 SVM Applications 􀂇 Committee machines 􀂇 PAC Learning 􀂇 Boosting 􀂇 “No Free Lunch” Theorem(Vapnik-Cheervonenkis (VC) Dimension 􀂇 Support Vector Machines 􀂇 SVM Applications 􀂇 Committee machines 􀂇 PAC Learning 􀂇 Boosting 􀂇 " No Free Lunch" Theorem)

codes for mimo-ofdm sous matlab

Lognormal distribution function.

说明：  删除卷积码的matlab仿真,广泛应用于通信系统中(delete the Matlab simulation, widely used in communication systems)

Markov Chain Monte Carlo and Gibbs Sampling

动态PCA方法，引入动态时间l，考虑变量本身的时序自相关性，同时还分析变量之间的时序相关性,把PCA方法应用到动态数据矩阵。 (Dynamic PCA method, introducing dynamic time l, consider the timing of the variable itself autocorrelation, also analyzed the correlation between the timing variables, the PCA method is applied to dynamic data matrix.)

针对jsp问题下的遗传算法，并应用matlab解决该问题的实例(Jsp problem, genetic algorithms, and applications matlab to solve the problem instance)

五子棋代码，智能实现人机对战。欢迎下载观看(The Gobang code, intelligent man-machine battle)

The MATLAB codes ELM with kernels (for both regression and multi-class classification) work linearly similarly to ELM with random hidden nodes. For the sake of convenience, the source codes of ELM with kernels are given separately.

Decompose image into subbands (undecimated wavelet), denoise, and recompose again. fh = decomp_reconst_wavelet(im,Nsc,daub_order,block,noise,parent,covariance,optim,sig) im : image Nsc: Number of scales daub_order: Order of the daubechie fucntion used (must be even). block: size of neighborhood within each undecimated subband. noise: image having the same autocorrelation as the noise (e.g., a delta, for white noise) parent: are we including the coefficient at the central location at the next coarser scale? covariance: are we considering covariance or just variance? optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0) sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise) Javier Portilla, Univ. de Granada, 3/03 Revised: 11/04