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Bubble sort - Bubble sort is a straightforward and simplistic method of sorting data that is used in computer science education.

冒泡排序-冒泡排序是一种简单的和简单化的方法,分类数据,用来在计算机科学教育。

They may also be asked to conduct categorical data analysis, robust estimation or survival analysis.

他们也可能会要求进行分类数据分析,稳健估计或生存分析。

Hierarchical linear models for categorical data are seldom applied in the field of psychological research in China.

分类数据的多层线性模型在我国的心理学研究中鲜有使用。

Ing method. We found, after statisticsing the 91 tested preface classification data′s result, that Stress=0.13481<0.20,DAF=0.86519>0.60,and the model draws up to match well and has the certain rationality. Organizational management structure of the Jin-merchant in the Ming and Qing dynasty could be divided into five dimensionali?鄄ties:(1)the form of inspiritment methods is diverse;(2)the personnel system is enlightened;(3)the principle of the personnel selecting is objective and strict;(4)strategic measure of human resource can keep on the develop?

在对91名被试的有序分类数据结果进行统计处理后发现,Stress=0.13481<0.20,DAF=0.86519>0.60,表明模型拟合良好,具有一定的合理性,且明清晋商组织管理的结构可分为5个维度:(1)形式多样的激励手段;(2)开明的用人制度;(3)客观严格的选拔理念;(4)可持续发展的人力资源战略措施;(5)严格规范的制度与考核管理。

Unsorted Data If you tried this query yourself you might have discovered that the data was displayed in a different order than shown here.

分类数据,如果你尝试过这个疑问你,你也可能发现,该数据显示,在一个不同的命令,比在这里显示。

A heuristic search strategy and a practical example are given.The result shows the consistent feature selection reduction method can eliminate the disaccords of the sample dataset,select the optimal feature variables,drop the dimension of the data and reduce the redundancy information effectively.

给出的启发式搜索策略和应用实例表明:一致化特征选择约简方法能有效消除分类数据集的不一致性,选择最优的特征变量、降低数据的维数、减少数据集中的冗余信息。

The method to configure RED parameters based on network properties is also presented. Simulations show that the modified Random Early Detection algorithm really performs better than the original Random Early Detection algorithm and can work for a much wider range of traffic. Finally, whether the assured service mechanisms i. e. an edge router tags every arriving packet of a TCP flow based service profile and core router drops packets differently at congestion using active queue management scheme is possible to provide a throughput consistent with target rate, is investigated under different network scenarios. Different factors including target rate tagged algorithm, RIO algorithm and TCP congestion control mechanism how to affect the throughput of an assured service TCP flow are examined. Based on a fluid model. we then derive a model of an assured service TCP flow, which is the function of round trip delay, packet loss rate and token bucket parameters. Based on this model, we observe that in some cases it is possible to regulate the token bucket parameters to gain a consistent throughput with the target rate, while in other cases there exist ranges of values of the achieved rate for which the token parameters have no influence. With the latter, some modified TCP congestion control mechanisms to assure TCP throughput are proposed.

最后,在区别服务网络结构内,研究了当前提出的一种边缘路由器根据服务合同标记、分类数据包,结合内部路由器采用主动队列缓冲管理机制区别处理数据包的确信服务机制是否可在不同网络情况下使TCP连接获得同目标速率相一致的、公平的吞吐量问题;指出影响TCP连接获得同目标速率相一致吞吐量的主要因素是目标速率大小、标记算法参数和TCP连接的拥塞控制算法;对此,基于流模型假设,首次推导出一个以端到端时延、数据包丢失率、漏桶参数为变量的确信服务TCP连接吞吐量模型;利用这个模型进行分析,得到主要结论是在一些情况下可通过设置合适的漏桶参数得到同目标速率更一致的吞吐量,而在另一些网络情况下不管如何设置漏桶参数都不能使TCP连接获得同目标速率一致的吞叶量;对于后一些情况,还初步讨论了改进TCP拥塞控制算法来改善TCP连接性能的方法。

For protein production data disaggregated data conversion.

用于蛋白质数据生成分类数据的转换。

This algorithm uses binary tree to construct the multiclass frame by decomposing the problem into three 2class classification problems, then uses Support Vector Machine optimizing the three hyperplanes.

该算法首先使用二叉树进行多类决策,将原始分类数据分解成3个二类分类问题,然后利用SVM进行二类分类,使3个分类超平面得到优化。

This paper introduces a kind of dynamic assignment method to decide the base classifier's weight. This method uses the current test data to calculate the degrees belong to each data subset that is misclassified and uses those degrees to assign weight to each base classifier.

介绍一种动态地对基分类器进行赋权重的方法,这种方法利用当前测试实例属于某个被错误分类数据子集的程度,并按照程度的大小给相应的基分类器赋适当的权重。

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