wireless_tools.29.tar

通过卷积神经网络进行人脸模式识别。。。。(Pattern recognition by means of convolutional neural networks)

工单系统666可以试试 非常给力哈哈哈哈哈哈哈(very good)

用ASP+SQL2005开发的人才招聘系统-Developed using ASP+ SQL2005 Recruitment System

主要实现遗传算法在高分辨率遥感影像中的道路提取阈值分割(The main road of genetic algorithm to achieve high-resolution remote sensing image extraction threshold segmentation)

萤火虫算法源于模拟自然界萤火虫在晚上的群聚活动的自然现象而提出的，每只萤火虫被视为解空间的一个解，萤火虫种群作为初始解随机的分布在搜索空间中，然后根据自然界萤火虫的移动方式进行解空间中每只萤火虫的移动。通过每一代的移动，最终使的萤火虫聚集到较好的萤火虫周围，也即是找到多个极值点，从而达到种群寻优的目的。(Firefly firefly algorithm derived the simulation of natural phenomena in the natural night clustering activities proposed, each firefly is regarded as a solution of the solution space, firefly population as the initial solution randomly distributed in the search space, and then the nature of fireflies moves to move the solution space per fireflies. By moving each generation, and ultimately to better gather fireflies firefly around, that is, to find a plurality of extreme points, so as to achieve the purpose of optimizing the population.)

标准的遗传算法程序源代码，解释很全，适合初学者，经过自己调试通过(Standard genetic algorithm source code, to explain it all, suitable for beginners, through their commissioning by)

这个是我看文献做的一个anfis(自适应神经网络模糊推理系统)的一个例子，已经表明注释，很容易上手(This is a code for anfis(Adaptive network-based fuzzy inference system),when you looke ie you can got it!)

使用jsp上载文件的例子-examples of upload files by using jsp

用TCPIP服务器客户端通讯源代码-with TCPIP communications client server source code

说明：  Probabilistic Programming and Bayesian Methods for Hackers是一本写给开发者的关于贝叶斯方法和概率问题的免费开源书。贝叶斯方法的用途十分广泛，在经济学上能找出一堆的例子。而在IT行业，机器学习是非常典型的一个应用。而机器学习也是本书作者写本书的一个重要的理由。 本书选择了Python作为编程语言，这一点都不奇怪，Python在科研和数据分析上的应用是非常方便和普遍的，比如大名鼎鼎的Numpy等。作者在本书中使用另一个库PyMC，它依赖于Numpy和SciPy(可选)。 如果你正从事数据分析和机器学习的研究，这是一本非常棒的书籍。它不仅介绍了贝叶斯的相关理论，告诉了我们一些这个伟大的理论没告诉我们的东西，更重要的是，他的文档基于ipython-notebook完成，这是一种输入代码实时在网页显示各种统计图的工具。图文并茂，十分生动。唯一的要求可能是需要有一定的数学功力和英语水平了。(The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion.)