资源列表
pyDogVsCat
- 识别率85%，kaggle上有名的猫狗大战算法，可以很方便的查看分类结果。每一个epch需要22s左右（GTX1050Ti 4G）(The recognition rate is 85%. The famous dog and dog algorithm on kaggle is very convenient for us to see the classification results. Each epch needs about 22s (GTX1050Ti 4G))
lle
- lle用来处理高维数据降维，经检验此程序运行效果很好(LLE is used to deal with dimensionality reduction of high-dimensional data. It is proved that the program runs well.)
simpleCNN
- 在anaconda+opencv+tensorflow平台下，利用简单的CNN卷积神经网络进行手写字符识别(Under the anaconda+opencv+tensorflow platform, we use simple CNN convolution neural network to handwritten character recognition.)
CNN
- 手写体识别的训练，采用卷积神经网络，附带数据集下载代码(The training of handwritten recognition is based on convolution neural network, and the download from the dataset.)
万门大学强化学习算法代码RW模型+TD模型
- 万门大学，强化学习，rw模型算法代码实现， V(CS) = V(CS) + A * ( V(US) * us - V(CS) * cs ) td模型， V(s{t}) = V(s{t}) + a[R(t+1) + rV{S(t+1)} - V{S(t)}](In the intensive learning of the University of Wan men, the RW algorithm, the python implementation, the algorithm f
CS_SVM_exmp
- 该程序是cs-svm的程序，用于对svm的算法优化(This program is a program of cs-svm, which is used to optimize the algorithm of SVM.)
A_star
- 能够进行路径规划，用于全局路径规划，是目前较为广泛的全局路径规划算法(Can perform path planning for global path planning. It is currently a relatively extensive global path planning algorithm.)
example2
- python的窗口界面实例，包含账号的登录与注册以及账号退出等功能。(The window interface instance of Python includes functions such as login and registration of account, and account exit.)
qpso
- 量子粒子群算法：因为粒子的位置和速度在量子空间中不能一起确定，所以用波函数表示粒子位置，通过蒙特卡罗方法求出粒子位置。gbest求解通过平均最好位置mbest得到。mbest是所有个体平均最优，通过它来求解粒子出现在相对点的位置，用L表示。而粒子的势表示位置的最终值，与L直接相关。(Quantum particle swarm optimization (QPSO): because the position and velocity of the particle can not be det
initialize_variables
- 实现神经网络多目标优化算法变量初始化的m文件(Initialization of multi-objective neural network)
神经网络
- 传统的TSP问题，用hopfeild神经网络简单解决(The traditional TSP problem is solved by Hopfeild neural network.)
lssvm_prediction
- LSSVM时间序列预测，基于matlab lssvm工具箱！推荐！！！！！(LSSVM time series prediction is based on MATLAB LSSVM toolbox. Recommend!!!!!)