GUImatlab

用于图像去噪实验界面，包括小波去噪，中值滤波等，程序有MMATLAB的gui界面，调试成功。，已通过测试。 (For image denoising experimental interface, including wavelet denoising, median filtering, procedures the gui interface MMATLAB, debugging success. , Has been tested.)

a code on pattern recognition which has pca as a dimentional reduction and knn as a classifier.

HYBRID APF IN INDUSTRIAL PLANT

分数阶傅里叶变换的matlab程序以及一些讨论。给出了源代码和一些图表，以及一些相关论文的论述(Fractional Fourier transform using Matlab.A discussion of the problem is also included.)

说明：  一些例题是m源代码，供大家参考与学习，希望对大家有所帮助。(Some examples are m source code for reference and learning, we want to help.)

很好用，大家可以下载之后运行的看看，做向量机的。(Useful, we can run to see after downloading, do vector machines.)

主要有高斯处理，通过MATLAB处理文件(Are Gaussian processing, processing of documents through the MATLAB)

内含visual C++和MATLAB代码，利用高斯呼喝模型进行背景建模，自适应确定高斯分量的个数，由Z.Zivkovic开发，相关文章是 Improved adaptive Gausian mixture model for background subtraction(Containing visual C++ and MATLAB code, the use of Gaussian background modeling feeding model, adaptive determine the number of Gaussian components, developed by Z.Zivkovic related article Improved adaptive Gausian mixture model for background subtraction)

含有C_Primer_Plus_(第五版)_中文版另外还有C_Primer_Plus的读书笔记(Containing C_Primer_Plus_ (Fifth Edition) _ Chinese version Another C_Primer_Plus reading notes)

AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.(AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.)