1) Minimization of J-Divergence Entropy
判别熵最小化
2) Minimum J-divergence entropy
最小判别熵
3) entropy minimization
熵最小化
4) standardized minimum distance discrimination method
标准化最小距离判别法
1.
Based on the concept of sample distance,the standardized minimum distance discrimination method is proposed in this paper which provides a quantitative criterion to assess the sediment-carrying capacity formulae and other formulae.
在评述水流挟沙力公式检验方法的基础上,以样本距离为基础,提出标准化最小距离判别法,将挟沙力公式检验资料转化到同一数量级,消除检验资料数值差异过大引起的误差,定量评价不同水流挟沙力公式。
5) entropy generation minimization
熵产最小化
6) J-divergence entropy
判别熵
1.
Feature was extracted with independent component analysis by maximizing J-divergence entropy (ICA-MJE), and then Support Vector Machine (SVM) was used to match two iris codes.
对虹膜纹理采用最大判别熵的独立分量分析(ICA-MJE)实现特征提取,通过支持向量机(SVM)完成模式匹配。
2.
Firstly,minimum J-divergence entropy was applied to extract the risk early-warning index and to find out the most effective feature by feature extraction.
利用判别熵最小化选取项目风险预警指标值,通过特征选取找出那些最有效的特征,研究出一种新的聚类算法——极大熵聚类算法,极大熵聚类算法是以概率为比例将任一指标向量分配给所有码向量,而不是仅仅只分配给与之最近的码向量,该算法是C-均值算法的一种推广。
补充资料：最小熵产生原理
见不可逆过程热力学。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条