Unsupervised Learning in Information Retrieval Using NOW Architectures

机译：使用NOW架构进行信息检索中的无监督学习

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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：与特定主题或用户查询相关的文档的检索效率和有效性可以通过相似文档的聚类以及引入并行策略来提高。在本文中，我们探索了无监督学习的使用，基于神经网络的聚类算法，以及NOW体系结构（一种低成本并行体系结构）的引入，并研究了其对信息检索的影响。

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《International Conference on Computer Aided Systems Theory(EUROCAST 2005); 20050207-11; Las Palmas de Gran Canaria(ES)》|2005年|P.179-188|共10页
会议地点 Las Palmas de Gran Canaria(ES)
作者
E.F. Combarro; J. Ranilla; R. Mones; N. Vazquez; I. Diaz; E. Montanes;
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Artificial Intelligence Center, University of Oviedo, Campus de Viesques, Gijon (Asturias) Spain;

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原文格式 PDF
正文语种 eng
中图分类机器辅助技术;
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6. Retrieval Practice Facilitates Judgments of Learning Through Multiple Mechanisms: Simultaneous and Independent Contribution of Retrieval Confidence and Retrieval Fluency [O] . Xi Chen, Mengting Zhang, Xiaonan L. Liu 2004

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Unsupervised Learning in Information Retrieval Using NOW Architectures

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