A4 Article in conference proceedings
Sparse minimal learning machine using a diversity measure minimization (2019)
Dias, M. L. D., Sousa, L. S., Rocha Neto, A. R. D., Mattos, C. L. C., Gomes, J. P.P., & Kärkkäinen, T. (2019). Sparse minimal learning machine using a diversity measure minimization. In ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 269-274). ESANN. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-178.pdf
JYU authors or editors
Publication details
All authors or editors: Dias, Madson L. D.; Sousa, Lucas S.; Rocha Neto, Ajalmar R. da; Mattos, César L. C.; Gomes, João P. P.; Kärkkäinen, Tommi
Parent publication: ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Conference:
- European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Place and date of conference: Bruges, Belgium, 24.-26.4.2019
ISBN: 978-2-87587-065-0
eISBN: 978-2-87587-066-7
Publication year: 2019
Pages range: 269-274
Number of pages in the book: 696
Publisher: ESANN
Publication country: Belgium
Publication language: English
Persistent website address: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-178.pdf
Publication open access: Other way freely accessible online
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/66333
Abstract
spaces. Such geometric con gurations are built upon two matrices created using subsets of input and output points, named reference points (RPs). The present paper considers an extension of the focal underdetermined
system solver (FOCUSS) for MMV linear systems problems with additive noise, named regularized MMV FOCUSS (regularized M-FOCUSS), and evaluates it in the task of selecting input reference points for regression
settings. Experiments were carried out using UCI datasets, where the proposal was able to produce sparser models and achieve competitive performance when compared to the regular strategy of selecting MLM input RPs.
Keywords: machine learning
Contributing organizations
Related projects
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 3
- Hämäläinen, Keijo
- Academy of Finland
- STRUCTURE PREDICTION OF HYBRID NANOPARTICLES VIA ARTIFICIAL INTELLIGENCE
- Kärkkäinen, Tommi
- Academy of Finland
Ministry reporting: Yes
Reporting Year: 2019
JUFO rating: 1