Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression
Hämäläinen, J., & Kärkkäinen, T. (2020). Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression. In ESANN 2020 : Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 691-696). ESANN. https://www.esann.org/sites/default/files/proceedings/2020/ES2020-181.pdf
Päivämäärä
2020Tekijänoikeudet
© The Authors, 2019
Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods, the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM), in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential, emphasizing the utility of the problem transformation especially with the EMLM.
Julkaisija
ESANNEmojulkaisun ISBN
978-2-87587-074-2Konferenssi
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningKuuluu julkaisuun
ESANN 2020 : Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningAsiasanat
Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM) in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential emphasizing the utility of the problem transformation especially with the EMLM. koneoppiminen
Alkuperäislähde
https://www.esann.org/sites/default/files/proceedings/2020/ES2020-181.pdfJulkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/43492634
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Profilointi, SA; Akatemiaohjelma, SALisenssi
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