GENETIC

一种使用经典GP算法计算关联维数的程序源代码，比较好用！(A classical GP algorithm using the correlation dimension of the source code, more easy to use!)

仿真未编码和进行Hamming编码的QPSK调制通过AWGN信道后的误比特率性能(Hamming coding and for the simulation are not encoded by QPSK modulation in AWGN channel, the bit error rate performance)

本文档内的源码 内容为图像分割技术在matlab中的代码 适合初学者和matlab爱好者(The source of the content within this document image segmentation code in MATLAB for beginners and Matlab lovers)

说明：  调试运行正常的遗传算法matlab程序，学习遗传算法的好途径(Debugging the normal running genetic algorithm matlab program, a good way to learn genetic algorithm)

ctrllab3.1 (ctrllab3.1 )

matlab环境下的张正友法标定，可以获得相机的内参外参(Zhang Zhengyou calibration method under matlab environment, you can get the camera' s internal reference external parameters)

Introduction: The goal of the HumanEva dataset is to develop new techniques, technology, and algorithms for the automatic articulated pose estimation and tracking of humans. Brown University is involved in an ongoing effort to develop this dataset of synchronized video and motion capture data. The dataset is meant to aid research uating algorithms for human motion estimation. Brown University has

蒙特卡罗算法matlab实例，介绍了matlab的蒙特卡罗算法的具体使用方法(Monte Carlo algorithm matlab example, introduced the specific use of the Monte Carlo algorithm matlab)

说明：  csharp和matlab混合编程的视频教程，很不错(csharp and mixed programming matlab video tutorial, very good)

In this paper, we introduce a new machine-learning-based data classification algorithm that is applied to network intrusion detection. The basic task is to classify network activities (in the network log as connection records) as normal or abnormal while minimizing misclassification. Although different classification models have been developed for network intrusion detection, each of them has its strengths and weaknesses, including the most commonly applied Support Vector Machine (SVM) method and the Clustering based on Self-Organized Ant Colony Network (CSOACN). Our new approach combines the SVM method with CSOACNs to take the advantages of both while avoiding their weaknesses. Our algorithm is implemented and evaluated using a standard benchmark KDD99 data set. Experiments show that CSVAC (Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both classification rate and run-time efficiency.