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entropy
- shannon Tsallis escort Tsallis renyi entropy and their relative entropy
entropy
- The functions include extensive Shannon and nonextensive Tsallis,escort Tsallis,and renyi entropy. the funcition names start with K_q_ indicate relative entropys Usage for all the seven functions:
RenyiEntropyThreshold_1d
- Renyi熵阈值的方法与Tsallis熵相类似，但是计算跟简单，这个是一篇文献的仿真，有实验结果。-Renyi entropy threshold method with the Tsallis entropy is similar, but the calculation is more simple, this is a literature of simulation and experimental results.
Artificial-Neural-Networks
- 摘要:回顾了神经网络理论发展的历史和现状。在此基础上，介绍并讨论了20世纪如年代神经网络研究的一些新进展。根据神经网络研究的特点，对人工神经网络今后的发展前景作了一定的评述， 人工神经网络的研究与发展必将对现代科学技术产生深远的影响.配准中基于像素和基于特征两类主要方法的优缺点 深入研究了互信息理论在医学图像配准领域的应用和实现机理以及互信息作为相似性测度的优缺点。本文着 重深入研究了Renyi广义互信息及其在图像配准中的作用，并针对互信息计算的复杂性，引入了广义近邻图嫡估计理论，并成功
renyi
- 实现任意长度的两数相加,其功能就不描述了，暴露代码-can add any numbers nomtter how long it is
熵阈值max-2d
- 用于图像分割，基于renyi熵阈值分割算法的最大熵阈值分割算法
Renyi
- 计算一维时间序列的Renyi熵，可作为脑电信号的特征提取方法，从而对脑电的复杂度进行分析-The Renyi entropy of one dimensional time series can be calculated as a feature extraction method of EEG signal, which can be used to analyze the complexity of EEG.
failure-diagnosis-method
- 文章描述的是EEMD、Renyi熵和SVM结合的滚动轴承的故障诊断过程。-The article describes the EEMD, Renyi entropy and SVM combination of rolling bearing fault diagnosis process
renyi
- 任意矩阵c++求逆程序，伴随矩阵法,可输入矩阵实现矩阵变换-Achieve arbitrary matrix inversion c++ procedures, using the adjoint matrix method
reyientrop
- 关于信号中求特征renyi entropy的代码，代码简单实用，便于理解，本人已经用过此代码。-About Renyi entropy features for the signal of the code, the code is simple and practical, easy to understand, and I have already used this code.
kernel_eca-master
- Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.(We introduce kernel entropy component analysis (kernel ECA) as a new method fo
lwuc6
- 蚁群算法,使用Renyi熵寻优,快速寻找最优解()
KECA_Journal_Article
- Robert Jenssen 撰写论文原文(We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated