Results 221 to 230 of about 12,314 (276)

Anchored and Propagated Updating Within Pseudoscientific Belief Systems. [PDF]

Ann N Y Acad Sci
García-Arch J, Ballestero-Arnau M, Barbería I, Rodríguez-Ferreiro J.   +3 more
europepmc   +1 more source

Tug of War Over Power: A Case Study on the Development of Professional-Informal Caregiver Tensions in Residential Dementia Care. [PDF]

Nurs Inq
Dohmen MDW, van den Eijnde C, Abma TA, Groot BC, Huijg JM.   +4 more
europepmc   +1 more source

Linear and categorical coding units in the mouse gustatory cortex drive population dynamics and behavior in taste decision-making. [PDF]

Elife
Lang L, Zheng CY, Blackwell JM, La Camera G, Fontanini A.   +4 more
europepmc   +1 more source

Learner-Educator Co-creation of Student Assessment in Health Professional Education Courses: A Scoping Review. [PDF]

Campbell Syst Rev
Killam LA, Godfrey C, Lock M, Gordon R, Ross-White A, Luctkar-Flude M.   +5 more
europepmc   +1 more source

Impact of Noise on Deep Learning-Based Pseudo-Online Gesture Recognition with High-Density EMG

Taleshi M, Yeung D, Trong MD, Negro F, Deny S, Vujaklija I.   +5 more
europepmc   +1 more source

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.
Rong Yan, Alexander G. Hauptmann, Rong Jin 0001   +2 more
core   +3 more sources

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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, Jianxun Liu
exaly   +2 more sources

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   +2 more sources