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Reactive web policing based on self-organizing maps

机译:基于自组织映射的反应式网络策略

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Almost without any doubt, the Internet and Web in particular, have brought about radical changes in information retrieval with unparallel benefits to organizations and individuals to gain access to vast amount of articles and documents. The focus on restricting inappropriate materials at their source is not well suited to the nature and open architecture of Internet, where information source maybe in a different legal jurisdiction than the recipient. This paper proposes a reactive approach based on SOM (Self-Organising Maps) neural network to deal with dynamic blocking and filtering of Internet contents. We describe the design and implementation of a web policing proxy (WebPolice) based on a Kohonen's neural network model that attempts to classify the web contents dynamically using competitive learning. The parameter setting of the network has been experimented to obtain the optimal classification rate and performance for the model.
机译:几乎毫无疑问,尤其是Internet和Web带来了信息检索方面的重大变化,为组织和个人提供了访问大量文章和文档的无与伦比的好处。在源头上限制不当材料的重点与Internet的性质和开放式体系结构不太匹配,因为Internet上的信息源可能与接收方的法律管辖权不同。本文提出了一种基于SOM(自组织映射)神经网络的反应性方法来处理Internet内容的动态阻止和过滤。我们描述了基于Kohonen神经网络模型的Web管制代理(WebPolice)的设计和实现,该模型试图使用竞争性学习对Web内容进行动态分类。已经对网络的参数设置进行了实验,以获得模型的最佳分类率和性能。

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