Recent Trends in Machine Learning with a Focus on Applications in Finance
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00564516" target="_blank" >RIV/67985807:_____/22:00564516 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/22:10452427
Result on the web
<a href="https://msed.vse.cz/msed_2022/article/577-Kalina-Jan-paper.pdf" target="_blank" >https://msed.vse.cz/msed_2022/article/577-Kalina-Jan-paper.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Recent Trends in Machine Learning with a Focus on Applications in Finance
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The 16th International Days of Statistics and Economics Conference Proceedings
ISBN
978-80-87990-29-2
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
187-196
Publisher name
Melandrium
Place of publication
Praha
Event location
Praha
Event date
Sep 8, 2022
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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