Putting Humans Back in the Loop: A Study in Human-Machine Cooperative Learning
Само за регистроване кориснике
2020
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Приказ свих података о документуАпстракт
This paper introduces a novel approach to human-machine collaborative
learning that allows for the chronically missing human learnability in the context of
supervised machine learning. The basic tenet of this approach is the refinement of a human
designed software model through the iterative learning loop. Each iteration of the loop
consists of two phases: (i) automatic data-driven parameter adjustment, performed by
means of stochastic greedy local search, and (ii) human-driven model adjustment based on
insights gained in the previous phase. The proposed approach is demonstrated through a
real-life study of automatic electricity meter reading in the presence of noise. Thus, a
cognitively-inspired non-connectionist approach to digit detection and recognition is
introduced, which is subject to refinement through the iterative process of human-machine
cooperation. The prototype system is evaluated with respect to the recognition accuracy
(with the highest digit recognition accu...racy of 94%), and also discussed with respect to the
storage requirements, generalizability, utilized contextual information, and efficiency.
Кључне речи:
human-machine cooperative learning / digit recognition / stochastic searchИзвор:
Acta Polytechnica Hungarica, 2020, 17, 2, 191-210Издавач:
- Budapest : Óbuda University
Институција/група
JakovTY - JOUR AU - Gnjatović, Milan AU - Maček, Nemanja AU - Adamović, Saša PY - 2020 UR - http://jakov.kpu.edu.rs/handle/123456789/1516 AB - This paper introduces a novel approach to human-machine collaborative learning that allows for the chronically missing human learnability in the context of supervised machine learning. The basic tenet of this approach is the refinement of a human designed software model through the iterative learning loop. Each iteration of the loop consists of two phases: (i) automatic data-driven parameter adjustment, performed by means of stochastic greedy local search, and (ii) human-driven model adjustment based on insights gained in the previous phase. The proposed approach is demonstrated through a real-life study of automatic electricity meter reading in the presence of noise. Thus, a cognitively-inspired non-connectionist approach to digit detection and recognition is introduced, which is subject to refinement through the iterative process of human-machine cooperation. The prototype system is evaluated with respect to the recognition accuracy (with the highest digit recognition accuracy of 94%), and also discussed with respect to the storage requirements, generalizability, utilized contextual information, and efficiency. PB - Budapest : Óbuda University T2 - Acta Polytechnica Hungarica T1 - Putting Humans Back in the Loop: A Study in Human-Machine Cooperative Learning VL - 17 IS - 2 SP - 191 EP - 210 DO - 10.12700/APH.17.2.2020.2.11 ER -
@article{ author = "Gnjatović, Milan and Maček, Nemanja and Adamović, Saša", year = "2020", abstract = "This paper introduces a novel approach to human-machine collaborative learning that allows for the chronically missing human learnability in the context of supervised machine learning. The basic tenet of this approach is the refinement of a human designed software model through the iterative learning loop. Each iteration of the loop consists of two phases: (i) automatic data-driven parameter adjustment, performed by means of stochastic greedy local search, and (ii) human-driven model adjustment based on insights gained in the previous phase. The proposed approach is demonstrated through a real-life study of automatic electricity meter reading in the presence of noise. Thus, a cognitively-inspired non-connectionist approach to digit detection and recognition is introduced, which is subject to refinement through the iterative process of human-machine cooperation. The prototype system is evaluated with respect to the recognition accuracy (with the highest digit recognition accuracy of 94%), and also discussed with respect to the storage requirements, generalizability, utilized contextual information, and efficiency.", publisher = "Budapest : Óbuda University", journal = "Acta Polytechnica Hungarica", title = "Putting Humans Back in the Loop: A Study in Human-Machine Cooperative Learning", volume = "17", number = "2", pages = "191-210", doi = "10.12700/APH.17.2.2020.2.11" }
Gnjatović, M., Maček, N.,& Adamović, S.. (2020). Putting Humans Back in the Loop: A Study in Human-Machine Cooperative Learning. in Acta Polytechnica Hungarica Budapest : Óbuda University., 17(2), 191-210. https://doi.org/10.12700/APH.17.2.2020.2.11
Gnjatović M, Maček N, Adamović S. Putting Humans Back in the Loop: A Study in Human-Machine Cooperative Learning. in Acta Polytechnica Hungarica. 2020;17(2):191-210. doi:10.12700/APH.17.2.2020.2.11 .
Gnjatović, Milan, Maček, Nemanja, Adamović, Saša, "Putting Humans Back in the Loop: A Study in Human-Machine Cooperative Learning" in Acta Polytechnica Hungarica, 17, no. 2 (2020):191-210, https://doi.org/10.12700/APH.17.2.2020.2.11 . .