L0deblur_test_wk

matlab几则设计经验，非常好用,word 文件(a few design experience for matlab, very easy to use)

模式识别与机器学习最新版本，很经典的一本书，适用于初学者学习。(Pattern recognition and machine learning the latest version of the classic book, suitable for beginners to learn.)

消噪滤波的一些程序对比。。。。。。。。。。(Some programs eliminate noise filtering contrast. . . . . . . . . .)

说明：  DPSD算法，用于滤波的，已经用于核磁共振技术很多年了，相当成熟了，希望对大家有用(DPSD algorithm for filtering, nuclear magnetic resonance technique has been used for many years, very mature, we hope to be useful)

此源代码,是一个一种基于MATLAB的程序源码，对2PSK过过程进行编程与开发，并绘出仿真波形，给出功率谱分析 可直接使用。 (This source code is a one kind of MATLAB-based program source code, right 2PSK over the process of programming and development, and plot the simulation waveforms are given the power spectrum analysis can be used directly.)

利用牛顿法，加速牛顿法，同伦变换求解非线性方程组的解(Newton method to accelerate the Newton method, homotopy transform Solving the Nonlinear Equations)

knn是一种分类算法，它是基于matlab的一种模式识别的经典算法，一般适用于样本个数较小的样本分类。(KNN, it is classification algorithm based on matlab a pattern recognition of the classic algorithm, generally applied in sample number of smaller sample classification. )

Bayes program 贝叶斯的课程设计，报告搜索bayes(Bayes Bayes program curriculum design, report search bayes)

一个Matlab实用教程，有编程基础，数值计算，矩阵，图形用户界面，接口等丰富的知识，一共31M，看大小也知道其含金量了(A Practical Guide Matlab, a programming foundation, numerical calculation, matrix, graphical user interface, interface, such as wealth of knowledge, a total of 31M, to see the size of its gold content is also aware of the)

机器学习中的垃圾邮件分类程序，用matlab做的。从以下链接下载垃圾邮件数据（spam data）：(数据已下载，放在spambase.zip) http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html 该数据包含57个邮件信息相关的变量，每条邮件可以被分类为垃圾邮件（Y=1）和非垃圾邮件（Y=0）。输出Y的值在文件中每一列的末尾。练习的目标是要预测电子邮件是否为垃圾邮件。 (Machine Learning spam classification procedures, using matlab to do. Data (spam data) from the following link to download the junk mail: (data has been downloaded, put spambase.zip) http://www-stat.stanford.edu/ ~ tibs/ElemStatLearn/index.html The data includes 57 e-mail messages related variables, each message can be classified as spam (Y = 1) and non-spam (Y = 0). Y value of the output end of each column in the file. The goal is to predict exercise email is spam.)