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DST
- DS证据推理的原始Dempster组合公式计算函数，详细使用见该文件内的帮助说明
belief_NN
- A neural network classifier based on Dempster-Shafer theory
IPToolbox
- 这是一个国外的DST的Matlab工具箱，很全面的反映了dempster-shafer理论，是学习DST的权威工具箱
DST
- DS证据推理的原始Dempster组合公式计算函数，详细使用见该文件内的帮助说明-DS evidential reasoning of Dempster combination formula original function, see the document in detail the use of the help that
belief_NN
- A neural network classifier based on Dempster-Shafer theory
IPToolbox
- 这是一个国外的DST的Matlab工具箱，很全面的反映了dempster-shafer理论，是学习DST的权威工具箱-This is a foreign DST toolbox of Matlab, it is a comprehensive reflection of dempster-shafer theory, is to learn from the authority of DST Toolbox
EM.java.tar
- EM 算法是 Dempster，Laind，Rubin 于 1977 年提出的求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行 MLE 估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据，截尾数据，带有讨厌数据等所谓的不完全数据(incomplete data)。需要weka的算法包支持。-EM algorithm is Dempster, Laind, Rubin in 1977 for the p
IPPToolbox
- exemple Dempster shafer
EM_Algorithm
- EM algorithm is to solute the problem of parameter maximum likelihood estimation by Dempster, Laind, Rubin in 1977. The EM algorithm can estimate maximum likelihood only through incomplete data set. -EM algorithm is t
smets
- dempster shafer fusion
evidence-theory-conflict-factor-k
- 证据理论Dempster规则中冲突因子K的求取。自己编的哦！-DEMPSTER-SHAFER EVIDENCE THEORY K
EM-
- EM检测 初级实例 多高斯混合 First, one of the k Normal distributions is selected at random. Second, a single random instance xi is generated according to this selected distribution. -EM algorithm (Dempster et al.1977)，a widely used
Artificial-Intelligence-DEMPSTER-SHAFER
- Artificial Intelligence with DEMPSTER-SHAFER
DS_fusion
- DS证据，功能：融合x,y两行向量(经典Dempster-Shafer组合公式)-DS evidence features: integration of x, y two row vectors (classical Dempster-Shafer combination formula)
dempster
- DS证据融合程序,DS证据理论中的组合规则-DS evidence fusion program, DS evidence theory combination rule
Dempster-Shafer
- 使用经典Dempster-Shafer组合公式融合x,y两行向量-fusion row vector x with row vector y using Dempster-Shafer
DS_Fusion
- 证据理论合成规则，能很好地处理不确定信息(The rules of Dempster synthesis can deal with uncertain information well)
Dempster-Shafer-master
- Support for normalized as well as unnormalized belief functions Different Monte-Carlo algorithms for combining belief functions Various methods related to the generalized Bayesian theorem Measures of uncertainty Meth
simple DS
- 经典证据理论融合公式，用于对两个数据源进行初级融合，效果较好(Dempster-Shafer.The fusion formula of classical evidence theory is used for primary fusion of two data sources, and the effect is good.)