matlab-examples

这个一些经典的数据挖掘matlab源程序，比较全。(some of the classic source Matlab data mining, comparative whole.)

说明：  里面包含CAPON、ML、MUSIC、RLS、LMS、循环MUSIC等算法，及算法比较(CAPON、ML、MUSIC、RLS、LMS and Cylic-MUSIC)

遗传算法matlab实现，还有倒位操作。(Genetic Alogrithm using matlab, as well as changing-seat operation.)

用有理多项式做Chebyshev逼近的一种直接方法的matlab程序(Direct Method for Chebyshev Approximation by Rational Functions)

设计滤波器的各种窗函数的源代码，非常有用，和大家分享，快来下载学习哦！！(A variety of window functions designed to filter the source code, very useful to share, come to learn oh download！！)

本系统主要是设计一个简单的比例放大器，使小车能以指定的速度运动。采用“自下而上”，“分层”建模思想而设计的。主要包括了理论分析与设计、仿真系统的创建、仿真结果、仿真操作说明、结论等五部分。使用Matlab7.1实现(This system is mainly to design a simple proportional amplifier, so the car can be specified speed of movement. " Bottom up" and " layered" modeling idea. Include theoretical analysis and design, the creation of the simulation system, the simulation results, simulation instructions, conclusions of five parts.)

e book for edge detection

程序分形分析工具箱 - FracLab2.04MATLAB包括拥有数百个内部函数的主包和三十几种工具包。工具包又可以分为功能性工具包和学科工具包。功能工具包用来扩充MATLAB的符号计算，可视化建模仿真，文字处理及实时控制等功能。学科工具包是专业性比较强的工具包，控制工具包，信号处理工具包，通信工具包等都属于此类。(Program fractal analysis toolbox- FracLab2.04MATLAB including internal functions with hundreds of primary packages and several 30 kits. Kits can be divided into functional disciplines toolkit and toolkit. Function tool kit used to expand the MATLAB symbolic computation, visualization, modeling and simulation, word processing, and real-time control functions. Discipline tool kit is relatively strong professional toolkit, Control Toolkit, signal processing toolkit, communications toolkit belong to this category.)

The aim of this application is to implement scalable mpeg type compression by varying DCT coefficients and finding minimum number of DCT coefficients that can give minimum acceptable quality.. This application only works for gray scale.

多核变换，稀疏变换，核变换自适应学习，图形图像变换(Description ----------- Multi-Kernel-Path package is a Matlab program that computes the entire regularization path for multiple-kernel learning problems. More precisely, it solves any learning problem with a smooth loss function and where the kernel itself is optimized over combinations of several basis kernels. It returns the solutions for ALL values of the regularization parameter.)