资源列表
K-Means
- matlab kmeans 程序说明与数据源代码-matlab kmeans source
k
- k mean algorithm implementation using random cluster centroid
AprioriAlgorithm.tar
- apriori的c#实现代码,利用该算法实现频繁模式的挖掘和关联规则的发现,此经典算法的类接API可以嵌入到自己开发的应用程序中应用。-the complementation of Apriori algorithm using c#
wavelet_test_animation
- 对于振动传感器采集的数据,利用小波变换,得到数据时频的动画。-For vibration data collected by sensors using wavelet transform to obtain frequency data animation.
ML_Metric-function
- 上传的代码为多标签数据分类的度量函数 用于对多标签数据分类进行效果上的度量-Upload code for the multi-label data classification function is used to measure multi-label classification data to measure the effect on
KMeans-master
- KMeans聚类算法的Java实现 KMeans聚类算法的Java实现-KMeans clustering algorithm implemented in Java KMeans clustering algorithm implemented in Java KMeans clustering algorithm implemented in Java
MATLAB-generate-fuzzy-rules
- MATLAB通过数据挖的方法,得到模糊控制规则,用来进行模糊控制。-MATLAB through data mining method to obtain a fuzzy control rule for fuzzy control.
k-means-Clustering
- simple k-means implementation with Persian comments
AdaBoost
- Adaboost是一种迭代算法,其核心思想是针对同一个训练集训练不同的分类器(弱分类器),然后把这些弱分类器集合起来,构成一个更强的最终分类器(强分类器)。-Adaboost is an iterative algorithm, the core idea is the same training set for training different classifiers (weak classifiers), then these weak classifiers together to f
linear-regression
- 使用matlab实现机器学习最常用的linear regression算法-Using matlab realize learning machine most commonly used linear regression algorithm
AdaBoost
- 用python实现的AdaBoost分类算法,文件是一个ipython notebook,可以直接用ipython/jupyter打开使用。内附简单测试数据集。 程序运行需要numpy库的支持。-An AdaBoost classifier implemented with Python.
k_means
- 这是数据挖掘中的k均值聚类算法,用java语言编写的,对于搞聚类的人士很有帮助-This is the data mining k-means clustering algorithm, using java language, for persons engaged in clustering helpful