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Human Feedback and Knowledge Discovery: Towards Cognitive Systems Optimization

Abstrakt

Current computer vision systems, especially those using machine learning techniques are data-hungry and frequently only perform well when dealing with patterns they have seen before. As an alternative, cognitive systems have become a focus of attention for applications that involve complex visual scenes, and in which conditions may vary. In theory, cognitive applications uses current machine learning algorithms, such as deep learning, combined with cognitive abilities that can broadly generalize to many tasks. However, in practice, perceiving the environment and adapting to unforeseen changes remains elusive, especially for real time applications that has to deal with high-dimensional data processing with strictly low latency. The challenge is not only to extract meaningful information from this data, but to gain knowledge and also to discover insight to optimize the performance of the system. We envision to tackle these difficulties by bringing together the best of machine learning and human cognitive capabilities in a collaborative way. For that, we propose an approach based on a combination of Human-in-the-Loop and Knowledge Discovery in which feedback is used to discover knowledge by enabling users to interactively explore and identify useful information so the system can be continuously trained to gain previously unknown knowledge and also generate new insights to improve human decisions.

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Wersja publikacji
Accepted albo Published Version
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Creative Commons: CC-BY-NC-ND otwiera się w nowej karcie

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Procedia Computer Science nr 176, strony 3093 - 3102,
ISSN: 1877-0509
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
de Oliveira C., Sanin C., Szczerbicki E.: Human Feedback and Knowledge Discovery: Towards Cognitive Systems Optimization// Procedia Computer Science -Vol. 176, (2020), s.3093-3102
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.procs.2020.09.179
Weryfikacja:
Politechnika Gdańska

wyświetlono 38 razy

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