Debunking material induction

In this paper, we present an explanatory objection to Norton's material theory of induction, as applied to predictive inferences. According to the objection we present, there is an explanatory disconnect between our beliefs about the future and the relevant future facts. We argue that if we recognize such a disconnect, we are no longer rationally entitled to our future beliefs.     

The effects of forest fragmentation on the population ecology and natural history of a funnel-web spider

Habitat fragmentation strongly affects the abundance, distribution, body size and population genetics of invertebrates. Urban growth in Brazil has led to severe fragmentation, especially in the Atlantic Forest and savannas. The effects of this fragmentation on the common funnel-web spider Aglaoctenus lagotis were examined in two forest fragments within the interior savanna: a smaller fragment within an urban environment and a larger fragment within a rural environment. The reproductive period occurred in October, coinciding with the beginning of the rainy season, when the species was aggregated in the two forest fragments. The smaller fragment contained a larger population, and the spiders had a larger average prosoma size and web area. The presence of a larger population in a smaller area within the urban centre may reflect a limited dispersal ability, reduced predator abundance or low interspecific competition. The larger prosoma length and web area in the smaller habitat fragment suggest greater resource availability and a higher probability of capturing prey in the urban environment. In both areas, a larger number of capture threads was positively correlated with the presence of inquiline spiders in the webs. The genetic data indicate close similarity between and within the two areas, indicating that the species has low genetic variability or that the areas studied, consistent with their proximity, have separated only recently. Most savannas and forests in midwestern Brazil have recently undergone severe fragmentation, and further studies of this nature are needed.     

基于模糊信息增益的图像相关性度量

针对图像中所含有的统计概率不确定信息以及模糊不确定信息,提出了一种综合图像概率信息与模糊信息的新模糊信息增益度量方法来比较目标图像与参考图像之间的相关度,从而为图像相关匹配与检索提供强有力的依据.实验结果表明,该方法是有效的.     

带线性约束的最小一乘问题

本文作为[3]的续篇,考察了带线性约束的最小一乘问题的解的存在唯一性,并制定了计算方案.该计算方案利用了每一轮选代的已有结果,其效率一般比较高.     

一种由三角模所诱导的粗糙集间的相似性度量方法

给出两粗糙集间一种新的相似度计算方法,并将其应用到模糊模式识别问题中,通过算例说明这种方法的可行性和有效性.     

一种由三角模诱导的粗糙集间的相似性度量方法

首先介绍了粗糙集、模糊集与三角模的概念,接着指出利用三角模可以诱导出相应的包含度,进而可得到一个相似度,提出了一种利用三角模对粗糙集间的相似性进行度量的新方法.最后借助算例验证了该方法的可行性.     

基于正交试验设计的区间数立体决策方法

为了减少决策信息损失,针对区间数决策问题,提出了一种立体决策方法。将m维区间数决策向量看作具有2m个顶点的超矩形。利用Ls(2m)正交表在超矩形上均匀、分散地选取s个顶点代表该超矩形,再根据不同超矩形对应顶点之间的相对熵度量超矩形之间的差异,最后利用各决策方案同理想解的贴近度进行决策方案排序。实例验证表明该文方法有效、可行。由于选取了多个多维实数点代表区间数决策向量,在丰富了决策信息的同时也增加了计算量。     

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests’ relationship, we not only consider users’ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model (I-FGM) to incorporate these factors. Experiments on two data sets (bookmarking and music network) demonstrate that this predictive method can achieve better results than the other three methods (ANN, NB, and SVM).     

Study of the Influence of Parent-brand Similarity,Association,and Complimentary Fit on Co-brand Attributes

In this paper,it investigates the factors affecting successive brand alliance in which two brands from different product categories are featured together to introduce a co-brand.Based on the brand alliance theories,the degree of association,similarity,and complimentary fit between parents brands are important factors in determining successive brand alliance.By using the 5P's,which are premium price,perceived quality,product features,performance,and perceived image,as the measuring scale in brand attributes ...     

基于用户移动行为相似性聚类的Markov位置预测

由于采集点丢失或出现新用户等原因,GPS轨迹数据往往具有稀疏性,使得基于单个用户数据的位置预测准确率较低.针对这种情况,提出了基于移动行为相似性和用户聚类的Markov位置预测方法.首先,基于Voronoi图和原始GPS轨迹进行区域划分,位置预测基于区域轨迹进行;其次,提出了同时考虑用户转移特性和用户区域特性的移动行为相似性计算方法;再次,根据移动行为相似性对用户进行聚类,并在聚类的用户组上采用一阶Markov模型进行位置预测,提高了位置预测的准确性.真实GPS轨迹数据上的实验表明了所提出方法的有效性.