无人机“蜂群”的蜂拥涌现行为识别与抑制机理

为了抵消无人机"蜂群"所具有的非对称作战优势，从反控制其协同飞行的角度出发，将"蜂群"描述为具有涌现性特征的复杂系统，剖析无人机"蜂群"蜂拥涌现行为的产生机理，首次建立基于f-散度的"蜂群"涌现性度量模型.根据蜂拥控制算法所遵循的启发式规则提出"蜂群"蜂拥涌现行为的抑制机理，首次建立了干扰条件下蜂拥控制的失效判别模型.通过仿真实验，分析和讨论了干扰强度、干扰时机对抑制"蜂群"蜂拥涌现行为的影响.     

基于Hellinger距离的混合数据集中分类变量相似度分析

分类变量的相似度分析是数据挖掘任务中的一个重要环节,现有的分类变量相似度算法中存在忽视变量差异、受不均衡分布影响严重、无法应用于混合数据集等缺点。为克服以上缺点,提出了一种基于Hellinger距离的分类变量相似度算法。该算法累加分类变量对应子集中不同属性变量的分布差异作为相似度,且支持混合数据集。将所提算法代入聚类算法并应用于UCI公共数据集,结果表明,该算法在准确度、有效性和稳定性上都有较大提高。     

Some aspects of error bounds in feature selection

In this paper we discuss various bounds on the Bayesian probability of error, which are used for feature selection, and are based on distance measures and information measures. We show that they are basically of two types. One type can be related to the f-divergence, the other can be related to information measures. This also clarifies some properties of these measures for the two-class problem and for the multiclass problem. We give some general bounds on the Bayesian probability of error and discuss various aspects of the different approaches.     

DESCRIPTION OF PROBABILISTICAL SYSTEMS WITH STRONGLY STATISTICALLY DEPENDENT ELEMENTS

We propose the f-divergences of two probability distributions as the measures of the organization of a probabilistic system with respect to its probabilistic uncertainty. A probabilistic system consist of stochastical objects on which random variables are defined which are statistically dependent. Using Shannon's f-divergence for the organization of a probabilistic system we express it in terms of the probability distributions of the element random variables and their statistical linkages. Then we find the maximum entropy of a probabilistic system if the statistical linkages between its elements are given as input data. We show that an important class of physical statistical systems can be described by the probabilistic systems of Gibbsian type.