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1)  synthetic minority over-sampling technique (SMOTE)
少数类过抽样算法
2)  sampling algorithm
抽样算法
1.
Furthermore, in terms of the character to composite the posteriors distribution,the paper design sampling algorithm for the conditional posteriors distribution of mean and covariance-matrix in normal distribution with monotone missing data configuration.
进一步用后验分布的组成特点,构造了单调缺失数据结构的正态分布的协方差矩阵和均值后验分布的抽样算法。
3)  oversampling parameter
过抽样参数
1.
The conception of improved Logistic-OSCM is described firstly,then the relationships between the balance perform- ance of the improved Logistic-OSCM sequence and initial value of sequence,oversampling parameter,the period of the sequence are analyzed in this paper.
本文阐述了改进型 Logistic-OSCM 的概念,分析了序列初始值、过抽样参数及序列长度这三个参数对改进型 Logistic-OSCM 序列平衡性能的影响。
4)  Gibbs Sampler Algorithm
Gibbs抽样算法
1.
There are many methods to discover motifs, and Gibbs Sampler Algorithm and EM Algorithm are considered to be the most effective and mature technology.
识别motif的方法很多,其中Gibbs抽样算法和EM算法是较为成熟和有效的方法。
5)  estimation sampling
推算抽样法
6)  graph sampling algorithm
图抽样算法
1.
Given this, this paper briefly introduces the network topology model and traditional graph sampling algorithms, then brings the graph sampling algorithm to topology simplicity, combines the graph sampling algorithm, the model of association degree based on focus-zone and the model of a
鉴于此,本文在简要介绍了Internet拓扑模型和传统图抽样算法基础上,将图抽样算法引入到网络拓扑图的简化中,将图抽样算法、基于焦点的网络拓扑关联度模型和基于焦点的网络拓扑抽象度模型相结合,提出了一种基于焦点的网络拓扑抽象方法,该方法能够在很大程度上减小网络拓扑的规模,并且能够保留原拓扑图的特性。
补充资料:生过相似过类
【生过相似过类】
 (术语)十四过之一。对于立者而难其无喻证明之过失也。
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
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