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Recent Trends in Machine Learning with a Focus on Applications in Finance
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SYSNO ASEP 0564516 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Recent Trends in Machine Learning with a Focus on Applications in Finance Tvůrce(i) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Neoral, Aleš (UIVT-O) RID, SAICelkový počet autorů 2 Zdroj.dok. The 16th International Days of Statistics and Economics Conference Proceedings. - Praha : Melandrium, 2022 / Löster T. ; Pavelka T. - ISBN 978-80-87990-29-2 Rozsah stran s. 187-196 Poč.str. 10 s. Forma vydání Tištěná - P Akce International Days of Statistics and Economics /16./ Datum konání 08.09.2022 - 10.09.2022 Místo konání Praha Země CZ - Česká republika Typ akce EUR Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova statistical learning ; automated machine learning ; metalearning ; financial data analysis ; stock market investing Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA22-02067S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 Anotace Machine learning methods penetrate to applications in the analysis of financial data, particularly to supervised learning tasks including regression or classification. Other approaches, such as reinforcement learning or automated machine learning, are not so well known in the context of finance yet. In this paper, we discuss the advantages of an automated data analysis, which is beneficial especially if a larger number of datasets should be analyzed under a time pressure. Important types of learning include reinforcement learning, automated machine learning, or metalearning. This paper overviews their principles and recalls some of their inspiring applications. We include a discussion of the importance of the concept of information and of the search for the most relevant information in the field of mathematical finance. We come to the conclusion that a statistical interpretation of the results of theautomatic machine learning remains crucial for a proper understanding of the knowledge acquired by the analysis of the given (financial) data. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2023 Elektronická adresa https://msed.vse.cz/msed_2022/article/577-Kalina-Jan-paper.pdf
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