资源列表
Machine-Learning-in-Action
- 分析常见的机器学习算法，并进行实际的应用，能够很好的熟练掌握-Analysis of common machine learning algorithms, and the actual application, can be a good grasp
MGMM_Particle-Filter
- 本文分别实现了整体模板更新和选择性子模块更新方法，以适应运动目标的运动姿态变化以及运动背景变化，并将其分别与粒子滤波目标跟踪算法相结合，以提高跟踪的鲁棒性。-This thesis studies and implements a total target model updating method and a selected sub-model updating method, and then combines it with the particle filter algorithm f
ksvd-omp
- 这是个基于matlab的ksvd-omp算法-it s a ksvd-omp
reconstitution
- reconstitution函数，该函数用来重构相空间。-The reconstitution function, which is used to reconstruct the phase space.
svmMLiA
- 机器学习实战中，SVM向量机算法的实现。包括必要的注解、分类效果的测试-Machine learning actual combat, achieve SVM vector machine algorithm. Including tests necessary notes, classification effect
bayes
- 机器学习实战中，实现贝叶斯分类算法。包括算法的实现，必要的注释，分类测试-Machine learning actual combat, achieve Bayesian classification algorithm. Including the implementation of the algorithm, as required notices, classification test
kNN
- 机器学习实战中，K近邻算法的实现。包括算法实现，算法分类测试-Machine learning combat, the realization of K nearest neighbor algorithm. Including the algorithm, the algorithm classification test
open-mirror
- 电力负荷预测在国民的生产生活方面发挥着重要职能。精确的电力负荷预测，对电力系统的生产安排、安全分析和经济调度发挥着极其重要的作用，而它的预测精度则影响着电力市场的社会和经济效益。-: U7535 u529B u753 u5B09 u5129 u5B09 Jiaotong University Wang Zhao
matlab-simlink
- :电力电子技术 西安交通大学 王兆安 书配套MATLAB/simulink 仿真-Power Electronics Technology, Xi an Jiaotong University Wang Zhao -: U7535 u529B u753 u5B09 u5129 u5B09 Jiaotong University Wang Zhao
Bland-Altman
- Bland-Altman算法实现，包含比值法和差值法，包含做图功能-Bland-Altman algorithm to achieve, including the ratio method and the difference method, including the map function
demo3
- 在demo中，用EKF和有噪声的EKF训练非线性、非平稳数据。-In this demo, I use the EKF and EKF with noise adaptation to train a neural network with data generated a nonlinear, non-stationary state space model. Adaptation is done by matching the innovations ensemble covariance
CNN
- CNN 卷积神经网络，是深度学习的理论基础，能帮助加深对深度学习的认识.-CNN is the basis of deep learning. It can help researches to get better understanding of deep learning.