Results 181 to 190 of about 10,903 (240)

Explainable active reinforcement deep learning improves lung cancer detection from CT images. [PDF]

Sci Rep
Nady G, Salem A, Badawy O, Abo-ElNour S.
europepmc   +1 more source

Socioaffective Dynamics of Psychopathy in Daily Life. [PDF]

Clin Psychol Sci
Vize CE, Scott LN, Kamarck TW, Wright AGC.   +3 more
europepmc   +1 more source

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Pseudo relevance feedback optimization

Information Retrieval Journal, 2021
We propose a method for automatic optimization of pseudo relevance feedback (PRF) in information retrieval. Based on the conjecture that the initial query’s contribution to the final query may not be necessary once a good model is built from pseudo relevant documents, we set out to optimize per query only the number of top-retrieved documents to be ...
Arampatzis A., Peikos G., Symeonidis S.
openaire   +2 more sources

Collaborative pseudo-relevance feedback

Expert Systems with Applications, 2013
Pseudo-relevance feedback (PRF) is a technique commonly used in the field of information retrieval. The performance of PRF is heavily dependent upon parameter values. When relevance judgements are unavailable, these parameters are difficult to set. In the following paper, we introduce a novel approach to PRF inspired by collaborative filtering (CF). We
Dong Zhou, Mark Truran, Jianxun Liu, Sanrong Zhang   +3 more
openaire   +1 more source

Hybrid pseudo-relevance feedback for microblog retrieval

Journal of Information Science, 2013
The microblog has become a new global hot spot. Information retrieval (IR) technologies are necessary for accessing the massive amounts of valuable user-generated contents in the microblog sphere. The challenge in searching relevant microblogs is that they are usually very short with sparse vocabulary and may fail to match queries.
Chen, Lin, Chun, Lin, Ziyu, Lin, Quan, Zou   +3 more
openaire   +2 more sources

Multimedia Search with Pseudo-relevance Feedback

2003
We present an algorithm for video retrieval that fuses the decisions of multiple retrieval agents in both text and image modalities. While the normalization and combination of evidence is novel, this paper emphasizes the successful use of negative pseudo-relevance feedback to improve image retrieval performance.
Yan, Rong, Hauptmann, Alexander, Jin, Rong   +2 more
openaire   +1 more source

Exploring term temporality for pseudo-relevance feedback

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011
As digital collections expand, the importance of the temporal aspect of information has become increasingly apparent. The aim of this paper is to investigate the effect of using long-term temporal profiles of terms in information retrieval by enhancing the term selection process of pseudo-relevance feedback (PRF).
Stewart Whiting, Yashar Moshfeghi, Joemon M. Jose   +2 more
openaire   +1 more source