1) random feature selection
随机属性选择
2) selective-random scan
选择性随机扫描
1.
By extending further the model, the behavior of worms which propagate in the form of selective-random scan is considered and it is concluded that in a close computer group the greater the number of vulnerable computers varies in each and every.
分析了蠕虫病毒在一个封闭的计算机群中传播的过程,提出了一个离散的蠕虫传播模型,并且把该模型和CodeRedv2蠕虫的真实传播数据进行了比较,通过比较发现该模型较好地反映了随机扫描蠕虫的传播规律·进一步把该模型做了推广,考虑选择性随机扫描(Selective-randomscan)蠕虫的传播规律,通过推广后的模型可以发现,在一个封闭的计算机群中,各个小网络中的易感主机数变化越大,蠕虫传播的速度越快
3) Stochastically Cluster-head Selection
随机性簇头选择
4) random selection
随机选择
1.
The application of random selection in ranked set sampling;
随机选择排序集抽样的应用
2.
A strong limit theorem on random selection for independent identical distribution random variables;
独立同分布随机序列随机选择的强极限定理的证明
3.
A strong limit theorem on random selection for the sequence of N-valued random variables;
N值随机序列的随机选择的强极限定理(英文)
5) stochastic choice
随机选择
1.
Then,some maximal element theorems in stochastic choice problems and other theorems with respect to incomplete preference are obtained.
在不完全偏好意义下获得了随机选择问题中的极大元定理和其它一些定理,统一和推广了许多已知结果。
2.
Stochastic utility theory and multinomial logit model are used to analyze stochastic choice in demand market,while the influence of region and brand is treated as stochastic variable.
将产地、品牌差异的影响视为随机变量,运用随机效用理论和多项式logit模型研究需求市场上产品随机选择问题,运用Nash均衡方法分析生产商之间、零售商之间的竞争行为,分别得到了供应链网络各层均衡及整体均衡的条件、经济解释和变分不等式模型,最后给出了求解算法和具体算例。
3.
A market equilibrium model is proposed for differentiated multi product with stochastic choice, in which, multinomial logit is used as the probabilities of the stachastic choice and the supply of differentiated products is subjected to the total demand function of all products.
研究了单个市场、一种类型、多种规格的产品随机选择情况下的均衡模型。
6) attribute selection
属性选择
1.
Research on Attribute Selection Algorithm Based on Classification Theory of Corelation between Attributes;
基于属性间相关性分类理论的属性选择方法研究
2.
This method cam minimize the attribute selection,and keep the ability to classify tax data without great changes.
该算法解决了原有基于属性重要度的启发式属性约简算法结果中存在冗余属性问题,实现了属性选择较小化,并保持原有数据分类能力不发生大的变化。
3.
At the same time,the solution using attribute selection and instance selection to realize the approximation mining is brought out,and the mining efficiency and result model.
同时,提出使用属性选择和实例选择方法实现近似挖掘的方案,并对该方案进行挖掘效率和结果模型准确性的分析评估。
补充资料：随机数和伪随机数
随机数和伪随机数
random and pseudo-randan numbers
随机数和伪随机数【喇间佣1 al川牌”山一喇闭..m.山娜;cJI了，a如曰e”nce，口oc月卿成.以叹“c月a】 数亡。(特别，二进制数:。)，其顺序出现，满足某种统计正则性(见概率论(probability Uleory)).人们是这样区别随机数(mndomn切mbe比)和伪随机数(PSeudo一mn由mn切mbe岛)的，前者由随机的装置来生成，而后者是用算术算法构造的.总是假设(出于较好或较差的理由)所得(或所构造)的序列具有频率性质，这些性质对于具有分布函数F(z)的某随机变量心独立实现的一个序列来说是“典型的”;因此人们称作根据规律F(习分布的(独立的)随机数.最经常使用的例子为:在区间【O，l]上均匀分布的随机数亡。，尸(亡。
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