Wyszukiwarka
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.
Cytowania
-
1
CrossRef
-
0
Web of Science
-
2
Scopus
Cytuj jako
Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- Licencja
-
otwiera się w nowej karcie
Słowa kluczowe
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 164 razy
Publikacje, które mogą cię zainteresować
Image Representation for Cognitive Systems Using SOEKS and DDNA: A Case Study for PPE Compliance
- C. Silva de Oliveira,
- C. Sanin,
- E. Szczerbicki
Context-Aware Indexing and Retrieval for Cognitive Systems Using SOEKS and DDNA
- C. De Silva Oliveira,
- C. Sanin,
- E. Szczerbicki
Smart Knowledge Engineering for Cognitive Systems: A Brief Overview
- C. Silva de Oliveira,
- C. Sanin,
- E. Szczerbicki
Cognition and Decisional Experience to Support Safety Management in Workplaces
- C. De,
- C. Sanin,
- E. Szczerbicki