资源列表
gradientDescent
- 机器学习中用于求解代价函数最小值的梯度下降算法-Machine Learning for solving the minimum cost function gradient descent algorithm
analytic-hierarchy-process
- 层次分析法，对于多传感器的最后判决问题，给予一定的分析与取舍问题。-Analytic hierarchy process (AHP), for the final decision of multiple sensors, to give a certain analysis and choice.
discrete-ABC
- 人工蜂群算法，模拟蜂群寻蜜的过程可以实现最优化-Artificial bee colony algorithm to simulate the process of honey bee colony optimization can be achieved
ID3
- 人工智能，ID3算法，c语言实现可以通过样例的训练生成实现决策树进行决策 -Artificial intelligence, ID3 algorithm, C language can be achieved through the example of the training decision tree decision making
To-predict
- matlab预测程序集合，适用于数学建模大赛，直接数据导入就可以用。包括灰色模型预测程序2个，gm10，greymodel，高斯混合模型mixture_of_gaussians，以及BP神经网络优化模型，遗传算法优化，Genetic，粒子群算法优化，PSO-Matlab to predict collection, suitable for mathematical modeling contest, data import can use directly. Including gray mo
11192016
- Simulink Diagram and Code for Identifying a Continues time Function using Neural Networks.
11122016
- Simulink Diagram and Code for Identifying a Discrete time Function using Neural Networks.
covs
- 局部Log.Euclidean协方差矩阵描述子 L2ECM SPD矩阵的空间并不是一个向量空间,而是一个黎曼流形。因此,传统 欧氏空间内的运算 例如欧氏距离、均值- Local Log-Euclidean Covariance Matrix (L2ECM) to represent neighboring image properties by capturing cor- relation of various image
biaoqing
- 对jaffe人脸库进行识别测试的主程序，将jaffe人脸库分为训练集和测试集两部分，首先对图片进行LBP+LPQ特征提取，然后svm分类识别，统计识别率-Jaffe face for the identification of the main test will jaffe face is divided into a training set and a test set of two parts, the first of LBP+LPQ image feature extractio
code
- 基于matlab的线性神经网络实现的matlab代码-Matlab based on the linear neural network to achieve the matlab code
Kalman--PID
- Discrete Kalman filter for PID control Reference kalman_2rank.m-Discrete Kalman filter for PID control
news-crawler
- 数据处理中爬虫代码，这是一个新闻爬取的Python实现代码，里面有两个文件，news_crawler.py是Python实现代码,News是数据。-Data Processing reptiles code, which is a news crawling Python implementation code, there are two documents, news_crawler.py is a Python implementation code, News data.