模式识别的一些预处理知识源程序-pattern recognition pretreatment some knowledge source

Running this funciton each time to compute the LBP-TOP distribution of one video sequence. Reference: Guoying Zhao, Matti Pietikainen, "Dynamic texture recognition using local binary patterns with an application to facial expressions," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6):915-928.-It is 3D LBP, which is publised in IEEE TPAMI, a top journal of computer vision and pattern recognition. After unzipping the package, you can find a project coded in C++. After complied it, you can use it directly. Some tested images are also attached in the package.

一个牛人的神经网络支持向量机程序,对于搞神经网络的朋友强烈推荐.第二部分-A wonderful program of svm code coming from a superman,and strong recommendation to someone who learn about pattern-recognition .The second part.

模拟退火算法实验，使用MATLAB实现。-The simulated annealing algorithm, using MATLAB experiment.

用bp实现的数字识别程序，用到了经典的bp算法，可移植到其他应用程序中去-bp achieve with the digital identification procedures, the use of classical bp algorithm can be transplanted to other applications go

平时学习时搜集的算法书,聚类,神经网络,蚁群算,遗传算法-some e-books about the algorithm

属性约简程序： 1将roughset数据库附加到SQLServer2000数据库中 2 在system.ini中设置SQLServer2000服务器ip,若在本地，则不必设置 3 在CTR表中输入决策表 4 启动roughset_getreduce.exe求约简和规则.-attribute reduction procedures : roughset database will be an additional database to SQLServer2000 2 syste m.ini set SQLServer2000 server ip, if the local, they do not need to set up three tables in the importation of CTR Decision Tables 4 start roughset_getreduce.e xe demand reduction and rules.

本程序利用遗传算法实现PID参数的优化问题.-This procedure using genetic algorithm optimization of PID parameters.

计算前提论域中模糊子集的C语言程序.大家可以参考。-On the premise of the calculation domain fuzzy subset C language program. Everyone can refer to.

神经网络（模式识别模块）软件开发包!!!可以识别任何事物，适用于图像识别、语音识别、自动控制等诸多领域，具有简单、易学、开发周期短、识别率高等特点。-neural network (pattern recognition module) software development kit! ! ! Nothing can be identified and applied to image recognition, voice recognition, automatic control and many other areas, which is simple, easy to learn, short development cycle, features high recognition rate.