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Joonas Hämäläinen
Joonas Hämäläinen
Faculty of Information Technology, University of Jyväskylä
Verified email at jyu.fi
Title
Cited by
Cited by
Year
Comparison of internal clustering validation indices for prototype-based clustering
J Hämäläinen, S Jauhiainen, T Kärkkäinen
Algorithms 10 (3), 105, 2017
1512017
A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles
S Malola, P Nieminen, A Pihlajamäki, J Hämäläinen, T Kärkkäinen, ...
Nature communications 10 (1), 3973, 2019
462019
Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods
A Pihlajamaki, J Hamalainen, J Linja, P Nieminen, S Malola, ...
The Journal of Physical Chemistry A 124 (23), 4827-4836, 2020
422020
Improving scalable K-means++
J Hämäläinen, T Kärkkäinen, T Rossi
Algorithms 14 (1), 6, 2020
292020
Minimal learning machine: Theoretical results and clustering-based reference point selection
J Hämäläinen, ASC Alencar, T Kärkkäinen, CLC Mattos, AHS Júnior, ...
Journal of Machine Learning Research 21 (239), 1-29, 2020
222020
Feature selection for distance-based regression: An umbrella review and a one-shot wrapper
J Linja, J Hämäläinen, P Nieminen, T Kärkkäinen
Neurocomputing 518, 344-359, 2023
162023
Feature ranking of large, robust, and weighted clustering result
M Saarela, J Hämäläinen, T Kärkkäinen
Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia …, 2017
122017
Mapping the challenges of HCI: An application and evaluation of ChatGPT and GPT-4 for cost-efficient question answering
J Oppenlaender, J Hämäläinen
arXiv preprint arXiv:2306.05036, 2023
102023
Scalable robust clustering method for large and sparse data
J Hämäläinen, T Kärkkäinen, T Rossi
European Symposium on Artificial Neural Networks, Computational Intelligence …, 2018
82018
Do randomized algorithms improve the efficiency of minimal learning machine?
J Linja, J Hämäläinen, P Nieminen, T Kärkkäinen
Machine Learning and Knowledge Extraction 2 (4), 533-557, 2020
72020
Scalable initialization methods for large-scale clustering
J Hämäläinen, T Kärkkäinen, T Rossi
arXiv preprint arXiv:2007.11937, 2020
42020
Initialization of big data clustering using distributionally balanced folding.
J Hämäläinen, T Kärkkäinen
ESANN, 2016
42016
Instance-based multi-label classification via multi-target distance regression
J Hämäläinen, P Nieminen, T Kärkkäinen
European Symposium on Artificial Neural Networks, Computational Intelligence …, 2021
32021
Improvements and applications of the elements of prototype-based clustering
J Hämäläinen
JYU dissertations, 2018
32018
Newton Method for Minimal Learning Machine
J Hämäläinen, T Kärkkäinen
Computational Sciences and Artificial Intelligence in Industry: New Digital …, 2022
22022
Problem transformation methods with distance-based learning for multi-target regression
J Hämäläinen, T Kärkkäinen
European Symposium on Artificial Neural Networks, Computational Intelligence …, 2020
22020
Minimal Learning Machine for Multi-Label Learning
J Hämäläinen, A Souza, CLC Mattos, JPP Gomes, T Kärkkäinen
arXiv preprint arXiv:2305.05518, 2023
12023
Knowledge Discovery from Atomic Structures using Feature Importances
J Linja, J Hämäläinen, A Pihlajamäki, P Nieminen, S Malola, H Häkkinen, ...
arXiv preprint arXiv:2303.09453, 2023
12023
Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces
A Pihlajamäki, J Linja, J Hämäläinen, P Nieminen, S Malola, ...
European Symposium on Artificial Neural Networks, Computational Intelligence …, 2021
12021
Au38Q MBTR-K3
J Linja, T Kärkkäinen, J Hämäläinen, P Nieminen
Zenodo, 2023
2023
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