贝叶斯分类

关于时间序列分析的matlab程序代码，对原有的工具箱的补充-on time series analysis Matlab code, the original supplementary Toolbox

初学者实现矩阵乘法，觉得矩阵乘法简单但很实用，写的不是很高效，可以下载下来研究研究哦。-beginners achieve matrix multiplication, a simple matrix multiplication think it is practical and writing is not very efficient and can be downloaded from studies oh.

信息论中唯一可译码的判断，比较实用。学习信息论的同学可以参考。-Information theory can only decode the judge, more practical. Learning students can refer to information theory.

关于阵列信号处理的一个C++代码，关于经典的MVDR波束形成算法。自适应Capon波束形成器(Capon Beamforming)的实现算法，具有MFC界面。该算法是先进的，广泛使用在信号处理和通信领域的有效波束形成算法。一个自适应Capon波束形成器(Capon Beamforming)的实现算法。该算法是先进的，广泛使用在信号处理和通信领域的有效波束形成算法。

实现Apriori算法基本功能,获取测试数据集的关联规则-Realize the basic functions of Apriori algorithm，Access to test data sets of association rules

用希尔算法实现的排序-Hill with the ranking algorithm

% Function: [class,type]=dbscan(x,k,Eps) % ------------------------------------------------------------------------- % Aim: % Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN) % ------------------------------------------------------------------------- % Input: % x - data set (m,n); m-objects, n-variables % k - number of objects in a neighborhood of an object % (minimal number of objects considered as a cluster) % Eps - neighborhood radius, if not known avoid this parameter or put [] % ------------------------------------------------------------------------- % Output: % class - vector specifying assignment of the i-th object to certain % cluster (m,1) % type - vector specifying type of the i-th object % (core: 1, border: 0, outlier: -1)

灰色模型预测是在数据不呈现一定规律下可以采取的一种建模和预测方法，其预测数据与原始数据存在一定的规律相似性,下面程序是灰色模型GM(1,1)程序二次拟合和等维新陈代谢改进预测程序, 程序能够运行出正确的结果，希望对各位有帮助。

在模式识别中，尤其需要对一些样本进行分类，聚类分析是常用的方法，本程序基于最小最大距离的聚类原则实现对样本的聚类-At pattern recognition, in particular a number of samples required for classification, clustering analysis is a commonly used method, the procedure based on the smallest maximum distance of the cluster principle implementation on sample clustering

机器学习决策树经典C4.5java源码单机版  包括信息商计算 和决策树图打印两大部分