Adaboost

将数据集转换为拉普拉斯矩阵，然后利用基于图论的谱聚类进行聚类。拉普拉斯矩阵采用高斯核函数，全连接方法计算。谱聚类擅长处理高维数据或非凸数据集。(The data set is transformed into Laplacian matrix, and then clustered by spectral clustering based on graph theory. The Laplacian matrix is calculated by using the Gauss kernel function and the full connection method. Spectral clustering is good at dealing with high-dimensional or non-convex data sets.)

FDTD three-dimensional CPML

Python实现Adaboost算法，数据集为horseColic马疝气病数据集，准确率和sklearn库中的adaboost算法一样。(Python implementation adaboost algorithm, the data set is horseColic horse hernia disease data collection, accuracy and sklearn library adaboost the same algorithm.)

基于混合信号的小波包分解技术在故障特征提取中的应用(Feature Extraction Using Multisignal Wavelet Packet Decomposition)

基于matlab实现的一个在线迁移学习算法OTL,(Based on matlab to achieve an online migration learning algorithm OTL,)

用Apriori算法挖掘出入侵检测数据集KDD99的数据关联性，从而检测出未知的攻击(Apriori algorithm is used to extract the data association of the intrusion detection data set KDD99, and the unknown attack is detected)

基于密度的局部离群点检测，使用于当全部样本点的密度不一致的情况(Local outlier detection based on density)

遗传算法优化小波神经网络的源程序 1 构造的非线性函数 位于nninit_test m 2 直接用WNN逼近非线性 Wnn_test()

说明：  从数据库获取车辆在一段时间内的所有行驶记录的相关数据，确定所需数据为GPS经纬度坐标和驾驶时长等，QB模型采用MDF的思想,其基本思想为:通过平均直接翻转距离函数定义两条轨迹之间的距离，两条轨迹需要具有相同的经纬度点数，具有相同点数的轨迹最大的优点是对轨迹距离成对计算，且相同轨迹之间具有更高的分辨率，对于轨迹聚类的结果有一定的优化。(Retrieved from the database cars all over a period of time, record the related data, determine the required data for the GPS latitude and longitude coordinates, and the driving time, QB model by adopting the idea of MDF, its basic idea is: flip directly by the average distance function definition of the distance between two trajectories, two tracks will have the same latitude and longitude points, and has the biggest advantages of the same points of trajectory track distance calculation in pairs, and has higher resolution, between the same trajectory for trajectory clustering results have certain optimization.)

说明：  数据挖掘中的binning算法，用于数据预处理(Binning algorithm in data mining for data preprocessing)