估计网络目录中分类主题的大小和演变

I. Anagnostopoulos, C. Anagnostopoulos
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引用次数: 1

摘要

本文提出了一种估计网络目录中分类网页种群演化的统计方法。该提案基于野生动物生物学研究中使用的捕获-再捕获方法,并根据在网络上进行实验所需的假设和修正进行了修改。在这些实验中,网页被比作动物,网页的特定类别被比作特定物种的动物,其丰度、出生率和存活率都是估计出来的。所遵循的捕获-再捕获模型是一种允许我们将所研究的种群视为开放的模型。因此,随着时间的推移,人口不断发展,这意味着新的网页被插入研究中,而其他网页被删除或变得不活跃,类似于迁移或死亡的自然过程。能够对网页进行分类的人工智能分类器,扮演着识别所研究物种的生物学家的角色。在我们的工作中,基于四种不同的真实分类案例,进行了四种不同的模拟,以评估基于web范式的模型的鲁棒性。本文提供了我们提出的基于web的捕获-再捕获模型的实现细节,以及它的初步评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating the size and evolution of categorised topics in web directories
In this paper a statistical approach for estimating the evolution of categorized web page populations in web directories is proposed. The proposal is based on the capture-recapture method used in wildlife biological studies and it is modified according to the necessary assumptions and amendments for conducting the experiments on the web. During these experiments, web pages are likened to animals and the specific categories of web pages are likened to particular species of animals whose abundance, birth and survival rates are estimated. The capture-recapture model followed is a model that allows us to consider the populations under study as open. Thus, in the course of time the population evolves, meaning that new web pages are inserted in the study, while others are removed or become inactive, resembling the natural processes of migration or death. Artificial intelligence classifiers, capable of categorizing web pages, play the role of the biologists who recognize the species under study. In our work, four different simulations were conducted in order to evaluate the robustness of the model followed on the web paradigm, based on four different real classification cases. The paper provides the implementation details of our proposed web-based capture-recapture model, along with its initial assessment.
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