Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Ana Kostovska"'
Autor:
Matej Petković, Luke Lucas, Jurica Levatić, Martin Breskvar, Tomaž Stepišnik, Ana Kostovska, Panče Panov, Aljaž Osojnik, Redouane Boumghar, José A. Martínez-Heras, James Godfrey, Alessandro Donati, Sašo Džeroski, Nikola Simidjievski, Bernard Ženko, Dragi Kocev
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-8 (2022)
Measurement(s) electric current Technology Type(s) current readings in spacecraft housekeeping telemetry Sample Characteristic - Environment outer space
Externí odkaz:
https://doaj.org/article/f24eda5597eb4c9f93340b8f0ca19f70
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Abstract Multilabel classification (MLC) is a machine learning task where the goal is to learn to label an example with multiple labels simultaneously. It receives increasing interest from the machine learning community, as evidenced by the increasin
Externí odkaz:
https://doaj.org/article/cb1b9cda916845a097319c3929a5fa50
Autor:
Ivica Dimitrovski, Ivan Kitanovski, Panče Panov, Ana Kostovska, Nikola Simidjievski, Dragi Kocev
Publikováno v:
Remote Sensing, Vol 15, Iss 9, p 2343 (2023)
We propose AiTLAS—an open-source, state-of-the-art toolbox for exploratory and predictive analysis of satellite imagery. It implements a range of deep-learning architectures and models tailored for the EO tasks illustrated in this case. The versati
Externí odkaz:
https://doaj.org/article/0a1d2998d19247d394a549a0acc62387
The code and figures for the publication "Assessing Module Importance in modCMA-ES and modDE"
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85644b6976da3faeef5816bf2eadc0c6
Publikováno v:
Applications of Evolutionary Computation ISBN: 9783031302282
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0365c32691695019a96fa20f156cdb96
https://doi.org/10.1007/978-3-031-30229-9_17
https://doi.org/10.1007/978-3-031-30229-9_17
Autor:
Ana Kostovska, Anja Jankovic, Diederick Vermetten, Jacob de Nobel, Hao Wang, Tome Eftimov, Carola Doerr
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031147135
17th Proceedings of Parallel Problem Solving from Nature-(PPSN) 2022
17th Proceedings of Parallel Problem Solving from Nature-(PPSN) 2022, 2022, Dortmund, Germany. pp.46-60, ⟨10.1007/978-3-031-14714-2_4⟩
17th Proceedings of Parallel Problem Solving from Nature-(PPSN) 2022
17th Proceedings of Parallel Problem Solving from Nature-(PPSN) 2022, 2022, Dortmund, Germany. pp.46-60, ⟨10.1007/978-3-031-14714-2_4⟩
International audience; Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform well for the particular setting. The selectio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7473007a382ba6dfa1bd61d32458cdf2
Selecting the most suitable algorithm and determining its hyperparameters for a given optimization problem is a challenging task. Accurately predicting how well a certain algorithm could solve the problem is hence desirable. Recent studies in single-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98d41ddf452dae46868110d3ab714554
Publikováno v:
Scientific reports. 12(1)
Multilabel classification (MLC) is a machine learning task where the goal is to learn to label an example with multiple labels simultaneously. It receives increasing interest from the machine learning community, as evidenced by the increasing number
Publikováno v:
Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material)
Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material)
Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material), Jul 2021, Lille (on line), France. ⟨10.1145/3449726.3459579⟩
GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
GECCO Companion
Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material)
Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material), Jul 2021, Lille (on line), France. ⟨10.1145/3449726.3459579⟩
GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
GECCO Companion
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable research. However, different platforms use different data models and format
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04d148b875187f74394edd21b15c2d99
https://hal.sorbonne-universite.fr/hal-03233907
https://hal.sorbonne-universite.fr/hal-03233907
Autor:
Ana Kostovska, Matej Petkovic, Tomaz Stepisnik, Luke Lucas, Timothy Finn, Jose Martinez-Heras, Pance Panov, Saso Dzeroski, Alessandro Donati, Nikola Simidjievski, Dragi Kocev
Publikováno v:
2021 IEEE 8th International Conference on Space Mission Challenges for Information Technology (SMC-IT).
We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs various machine learning algorithms for multivariate time series analyses, classification, reg