Výsledky vyhledávání - "Kutalmış Coşkun"

Autonomous acquisition of arbitrarily complex skills using locality based graph theoretic features: a syntactic approach to hierarchical reinforcement learning

Autor: Borahan Tumer, Zeynep Kumralbaş, Kutalmış Coşkun, Semiha Hazel Çavuş

Publikováno v: Evolving Systems.

© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.With the growing state/action space, learning a satisfactory policy for regular Reinforcement Learning (RL) algorithms such as flat Q-learning be

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bf69cbd73d22dc9d5e1c65a89f6fe43
https://doi.org/10.1007/s12530-022-09478-6

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Learning under concept drift and non-stationary noise: Introduction of the concept of persistence

Autor: Kutalmış Coşkun, Borahan Tümer

Publikováno v: Engineering Applications of Artificial Intelligence. 123:106363

Learning from noisy data is a challenging task especially when the system under consideration has a non-stationary nature. The source of the noise is often assumed to be stationary, however the severity or characteristics of noise may also be time-va

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d96ba57b742c3a8f8cc27ad41f20145
https://doi.org/10.1016/j.engappai.2023.106363

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An adaptive estimation method with exploration and exploitation modes for non-stationary environments

Autor: Borahan Tumer, Kutalmış Coşkun

Publikováno v: Pattern Recognition. 129:108702

Dynamic systems are highly complex and hard to deal with due to their subject-and time-varying na-ture. The fact that most of the real world systems/events are of dynamic character makes modeling and analysis of such systems inevitable and charmingly

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f50dc60803cbe4251c9348ff8da1c29b
https://doi.org/10.1016/j.patcog.2022.108702

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A Syntactic Pattern Recognition Based Approach to Online Anomaly Detection and Identification on Electric Motors

Autor: Kutalmış Coşkun, Zeynep Kumralbaş, Hazel Çavuş, Borahan Tümer

Publikováno v: Lecture Notes in Computer Science
Lecture Notes in Computer Science-Pattern Recognition
Lecture Notes in Computer Science ISBN: 9783031167874
Pattern Recognition. DAGM GCPR 2022. Lecture Notes in Computer Science

Online anomaly detection and identification is a major task of many Industry 4.0 applications. Electric motors, being one of the most crucial parts of many products, are subjected to end-of-line tests to pick up faulty ones before being mounted to ot

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80964b72f45fa291d964af4fa4a10b1f

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