V
Vladislav Khramtsov
Researcher at University of Kharkiv
Publications - 19
Citations - 160
Vladislav Khramtsov is an academic researcher from University of Kharkiv. The author has contributed to research in topics: Selection (genetic algorithm) & Galaxy. The author has an hindex of 6, co-authored 18 publications receiving 71 citations.
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Journal ArticleDOI
KiDS-SQuaD. II. Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars
Vladislav Khramtsov,Alexey V. Sergeyev,Alexey V. Sergeyev,Chiara Spiniello,Chiara Spiniello,Crescenzo Tortora,Nicola R. Napolitano,Nicola R. Napolitano,Adriano Agnello,Fedor Getman,Jelte T. A. de Jong,Konrad Kuijken,Mario Radovich,Huanyuan Shan,Valery Shulga,Valery Shulga +15 more
TL;DR: In this paper, the authors presented a new, automatic object classification method based on the machine learning technique, which was specifically trained with the aim of creating a sample of extragalactic sources that is as clean of stars as possible.
Journal ArticleDOI
KiDS-SQuaD II: Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars
Vladislav Khramtsov,Alexey V. Sergeyev,Alexey V. Sergeyev,Chiara Spiniello,Chiara Spiniello,Crescenzo Tortora,Nicola R. Napolitano,Nicola R. Napolitano,Adriano Agnello,Fedor Getman,Jelte T. A. de Jong,Konrad Kuijken,Mario Radovich,Huanyuan Shan,Valery Shulga,Valery Shulga +15 more
TL;DR: In this paper, the authors presented a new, automatic object classification method based on machine learning technique, which was specifically trained with the aim of creating a sample of extragalactic sources as clean as possible from stars.
Book ChapterDOI
Astrometric Reduction of the Wide-Field Images
TL;DR: The research results showed that the new proposed algorithm for astrometric reduction allows performing the reduction into the system of reference catalogue with the highest accuracy level.
Journal ArticleDOI
Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach
I. B. Vavilova,D. V. Dobrycheva,M. Yu. Vasylenko,M. Yu. Vasylenko,Andrii Elyiv,O. V. Melnyk,Vladislav Khramtsov +6 more
TL;DR: In this paper, the results of a binary automated morphological classification of galaxies conducted by human labeling, multi-photometry diagrams, naive Bayes, logistic regression, support vector machine, random forest, k-nearest neighbors, and five supervised machine learning methods are presented.
Journal ArticleDOI
Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach.
I. B. Vavilova,D. V. Dobrycheva,M. Yu. Vasylenko,M. Yu. Vasylenko,Andrii Elyiv,O. V. Melnyk,Vladislav Khramtsov +6 more
TL;DR: In this paper, a binary automated morphological classification of galaxies conducted by human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation is presented.