1) contiguity of probability measures
概率测度近邻性
2) Probabilistic Strongest Neighbor Filter
概率强近邻
1.
The proposed approach,based on Probabilistic Strongest Neighbor Filter,integrates the uncertainty of target existence and applies independent Markov chains in the general Bayesian framework.
该算法在概率强近邻算法的基础上结合目标存在的不确定性,运用了贝叶斯结构中的独立马尔可夫链,并将其性能与传统算法进行了比较,仿真结果显示了新算法的有效性和优越性。
3) conceptual contiguity
概念邻近性
1.
Currently,scholars have focused on the cognitive study of metonymy from the perspective of conceptual contiguity,which is grounded on the traditional structuralist definition of metonymy and regarded as its extension.
国内外一些学者主要从概念邻近性对转喻进行认知研究。
4) probabilistic nearest neighbor standard function
概率最近邻域
1.
To expand the application of distributed multisensor, based on probabilistic nearest neighbor standard function, distributed multisensor probabilistic nearest neighbor standard function was proposed.
为进一步扩大分布式多传感器的应用范围,基于概率最近邻域思想,研究了分布式多传感器概率最近邻域算法,并对其与分布式多传感器联合概率数据互联算法进行了性能仿真分析。
5) singularity of probability measures
概率测度奇异性
6) probability measure
概率测度
1.
Data mining expand model research based on probability measure;
基于概率测度的数据挖掘扩展模型研究
2.
The average direct theorems obtained means that the order of average error bounds of best approximation is determined by the structural properties of functions in a set in which the probability measure is supported.
讨论了Ｗｉｅｎｅｒ空间上连续函数最佳逼近平均误差界的阶，它由概率测度及其所支撑的集合上其函数的结构性质决定。
补充资料：概率测度
概率测度
probability measure
概率测度I声加谕ty~;即po.翻oc，朋Mepa]，概率分布(pro恤bi」jty曲州bu石on)，概率(pm恤城勿) 一个在非空集合Q(基本事件空间)的子集(事件)类形成的口域(即对可数次集论运算封闭)了上定义的实、非负函数尸满足 。(。卜l及尸(叠A，)一‘氢尸‘A‘，，如果对i尹j，A‘自A，二必(。可加性). 概率测度的例子.1)。={1，2};了是0的一切子集的类:p({l})二p({2})=1/2(这一概率测度对应于掷一颗对称的硬币组成的随机试验;如果正面对应于1而反面对应于2，掷得正面(反面)的概率是l/2); 2)。二{。，l，…};了是。的所有子集的类; 。，r，、、又人 P(万k飞、=牛，e一氏. k!其中又>0(P(血期1分布(Poisson distribu石。n)); 3)几二R‘;了是R’的Borel子集类; 尸‘“，一言丁一呱(正态分布(non斑21 distnbution)); 4)。=C。【O，l]是【o，1]上连续且在原点为零的实函数空问;了是关于一致收敛拓扑的BOrel子集类;尸是由公式 P(兀:a<二(t)
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参考词条