完整的能量熵的计算例程

聚类分析工具箱 亚历山大博士写的，用于聚类分析，功能比较全(Cluster Analysis and Cluster Ensemble Software ClusterPack is a collection of Matlab functions for cluster analysis. It consists of the three modules ClusterVisual, ClusterBasics, and ClusterEnsemble as described in the following. They are a selection out of my personal codebase for machine learning research. They contain general clustering algorithms as well as special algorithms developed in my research as indicated in the README files)

说明：  EM 算法 本程序实现了批量EM算法，可利用该程序进行批量EM算法的仿真(EM algorithm to achieve the bulk of the EM algorithm procedure)

基于matlab的成绩统计表，输入成绩，自行统计最高分，最低分，均分等(Matlab-based performance statistics, enter grades, self Statistics highest score, lowest score, sharing, etc.)

本书籍讲诉的是objective-C编程中的ARC规范，是高手的必看书籍。(This book stresses that the ARC specification of objective-C programming, is the master of the books.)

kNN最近邻算法实现无线室内定位，给出了全部的源码，并对其定位精度给出图示结果(KNN nearest neighbor algorithm implementation wireless indoor positioning, all of the source code is given, and the positioning accuracy of the graphic results is given )

matlab基础程序——代数方程解法，适合初学者(matlab-based program- calculus and differential equations, suitable for beginners)

在某一网络拓扑结构中,检验是否使每一个节点都保持连通状态,在电网规划中有重要作用(In a network topology, the test is to enable each node to maintain connectivity of a state, in the grid plays an important role in planning)

photo voltaic cell modell can be used in matlab simulink

一种基于半监督的svm的图像分类方法。该方法通过聚类核的方法利用无标记样本局部正则化训练核的表达式。这种方法通过图像直接学习一个自适应的核。该程序仿真的是文章：Semi-supervised Remote Sensing Image Classification with Cluster Kernels。大家可以参考下。(A semi-supervised SVM is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image, and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictionsds)

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