DDC_LFM

Pegasos-原始估计分梯度求解支持向量机“ 是一种原始的优化支持向量机分类算法解决问题。 请参阅进一步的参考。("Pegasos-Primal Estimated sub-Gradient SOlver for SVM" is a primal optimization problem solver in Support Vector Machine classification algorithm. See the paper for further reference.)

对最短路径算法的研究，是matlab的，用于研究最短路径算法(Study on the algorithm of the shortest path, matlab algorithm for the shortest path,)

说明：   第01章 DB2应用开发概述.rar(Chapter 01 outlines the DB2 application development. Rar)

LDA 人脸识别的 程序段 大家下着看看吧 对大家有帮助的(LDA face recognition under the program segment we take a look at the bar for all of us help)

关于MATLAB编程用两种方法模拟光学实验(MATLAB programming on the optics experiments using two kinds of simulation)

利用最陡下降法分离宽带和窄带信号，实现谱线增强(you can separate broadband and narrowband signal by the steepest descent method to achieve line enhancement )

TH-PPM-UWB的simulink仿真框图以及各个部分的S函数(TH-PPM-UWB in the simulink simulation block diagram and various part of the S function)

Least squares sinusoid fit algorithm described in IEEE Standard for Digitizing Waveform Recorders (IEEE Std 1057): Algorithm for three-parameter and four-parameter least squares fit to sinewave data using matrix operations. The algorithm is (in most cases) really quick. If the signal frequency is a guess, it has to be close to true frequency value. For further information, consult IEEE Std 1057 and/or IEEE Std 1241 documentation. Now also fits complex sinusoids i.e. phasors with noise and o

混沌序列中不同映射的实现，matlab语言编写(Chaos in different mapping implementations , matlab language )

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