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Unsupervised Learning in Information Retrieval Using NOW Architectures

机译:Information Restival的无监督学习使用现在使用现在的架构

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The efficiency and effectiveness of the retrieval of documents which are relevant to a certain topic or user query can be improved by means of the clustering of similar documents as well as by introducing parallel strategies. In this paper we explore the use of unsupervised learning, using clustering algorithms based on neural networks, as well as the introduction of NOW Architectures, a kind of low-cost parallel architecture, and study the impact on Information Retrieval.
机译:通过相似文档的聚类以及引入并行策略,可以提高与某个主题或用户查询相关的文档检索的效率和有效性。在本文中,我们探讨了使用基于神经网络的聚类算法的无监督学习,以及现在架构的引入,一种低成本并行架构,以及研究对信息检索的影响。

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