资源列表
n 人有限非合作博弈计算一个纳什均衡
- n 人有限非合作博弈计算一个纳什均衡 N human finite non cooperative game for computing a Nash equilibrium(N human finite non cooperative game for computing a Nash equilibrium)
MvDA
- 这是关于图像处理的MVDN算法，多视图线性判别分析(This is the image processing MVDN algorithm, multi-view linear discriminant analysis)
tsp_ga
- GUI界面全程显示迭代求取过程，改进遗传算法求解TSP问题(GUI interface to display the entire strike iterative process, improved genetic algorithm for TSP)
IMM6
- 一种基于扩展的增量流形学习算法IMM-ISOMAP matlab源代码，数据也包含在里面了 (An incremental manifold learning algorithm based on IMM-ISOMAP matlab source code, the data is also included in the)
opnet仿真实例（5个）OPNET BOOK CODE
- opnet仿真的几个例子，里面有详细的功能介绍和流程(OPNET simulation of several examples, which have detailed functional introduction and process.)
13898405hvdc-simulink
- 主要用于电力系统的改善，电能质量，波形的改善，抑制电力系统的振荡，直流输电(It is mainly used for power system improvement, power quality, waveform improvement, and suppression of power system oscillation.)
数据挖掘导论
- 《数据挖掘导论》经典电子书教程，课后习题答案，含丰富讲解。("Introduction to data mining" classic e-book tutorial, after-school exercise answers, rich explanation.)
exercise_ZSL-master
- An exercise of zero-shot learning, including DAP, IAP, DeSIVE, SCoRe. By Chengzhe XU, Linyang HE, Yixu GAO
yolo_tensorflow
- 基于YOLO的目标检测, 对十种物体进行识别和定位。(object detection based on YOLO, perform recognition and location to ten types of objects.)
Chapter04
- 基于tensorflow 的神经网络的损失函数，学习率，正则化，滑动平均等方法(Method of loss function, learning rate, regularization and sliding average of neural network based on tensorflow)
基于SOM的数据分类
- SOM神经网络也属于自组织型学习网络，只不过更特殊一点它属于自组织特征的映射网络。该网络是由一个全连接的神经元阵列组成的无教师，自组织，自学习的网络。(SOM neural network also belongs to self-organizing learning network, but more specifically, it belongs to self-organizing feature mapping network. The network is a non-teache
DenseNet-master
- 这篇文章是CVPR2017的oral，非常厉害。文章提出的DenseNet（Dense Convolutional Network）主要还是和ResNet及Inception网络做对比，思想上有借鉴，但却是全新的结构，网络结构并不复杂，却非常有效！众所周知，最近一两年卷积神经网络提高效果的方向，要么深（比如ResNet，解决了网络深时候的梯度消失问题）要么宽（比如GoogleNet的Inception），而作者则是从feature入手，通过对feature的极致利用达到更好的效果和更少的参数。(