1) clustering by minimal separation distance
最小间隔聚类
1.
The approach of clustering by minimal separation distance can select the SVM whose separation margin is minimal based on the bottom-up route,and hierarchically cluster every two input samples according to their categories at each node.
根据分析结果,提出了一种基于SVM最大间隔分类、最小间隔聚类构造H-SVMs的新方法。
2) maximum margin clustering
最大间隔聚类
1.
Clustering is a new method of modeling background which applied in video monitoring system;the experimental results of MMC(maximum margin clustering) are relatively good,but the disadvantage of this method is great for the computer memory,and calculation of high demand.
聚类是应用于视频监控系统背景建模的新方法,其中MMC(最大间隔聚类)方法是实验效果相对较好的方法,但是这种方法的缺点是运算量大、对计算机内存的要求较高。
4) min cluster
最小聚类
1.
By working out such sample data the time complexity of figuring out min cluster is effectively reduced.
这些样本点的求得能有效减少求解最小聚类的时间复杂度。
5) minimum-distance code
最小间隔码
6) minimum phase clearance
相间最小间隔
补充资料：最小辐亮度与最小辐照度(见核爆炸火球)
最小辐亮度与最小辐照度(见核爆炸火球)
minimum-brightness and minimum-irradiance
zuixiao fuliangdu yu zuixiaofu乙haodu最小辐亮度与最小辐照度(minimum-brightness and而nimum一irradianee)见核爆炸火球。
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