1 resultado para Portfolio Shares
em Massachusetts Institute of Technology
Filtro por publicador
- JISC Information Environment Repository (6)
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- ABACUS. Repositorio de Producción Científica - Universidad Europea (2)
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- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (2)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (3)
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- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archive of European Integration (4)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (18)
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- Biblioteca de Teses e Dissertações da USP (1)
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- Center for Jewish History Digital Collections (1)
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- Comissão Econômica para a América Latina e o Caribe (CEPAL) (4)
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- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (61)
- Queensland University of Technology - ePrints Archive (257)
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- Repositório digital da Fundação Getúlio Vargas - FGV (18)
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- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (5)
- Savoirs UdeS : plateforme de diffusion de la production intellectuelle de l’Université de Sherbrooke - Canada (5)
- School of Medicine, Washington University, United States (1)
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- Universidad de Alicante (3)
- Universidad del Rosario, Colombia (16)
- Universidad Politécnica de Madrid (3)
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- Universidade Metodista de São Paulo (2)
- Universidade Técnica de Lisboa (3)
- Universitat de Girona, Spain (1)
- Université de Lausanne, Switzerland (1)
- Université de Montréal, Canada (3)
- University of Canberra Research Repository - Australia (2)
- University of Connecticut - USA (3)
- University of Michigan (22)
- University of Queensland eSpace - Australia (4)
- University of Southampton, United Kingdom (10)
- University of Washington (1)
- WestminsterResearch - UK (5)
Resumo:
The task in text retrieval is to find the subset of a collection of documents relevant to a user's information request, usually expressed as a set of words. Classically, documents and queries are represented as vectors of word counts. In its simplest form, relevance is defined to be the dot product between a document and a query vector--a measure of the number of common terms. A central difficulty in text retrieval is that the presence or absence of a word is not sufficient to determine relevance to a query. Linear dimensionality reduction has been proposed as a technique for extracting underlying structure from the document collection. In some domains (such as vision) dimensionality reduction reduces computational complexity. In text retrieval it is more often used to improve retrieval performance. We propose an alternative and novel technique that produces sparse representations constructed from sets of highly-related words. Documents and queries are represented by their distance to these sets. and relevance is measured by the number of common clusters. This technique significantly improves retrieval performance, is efficient to compute and shares properties with the optimal linear projection operator and the independent components of documents.