Articles with public access mandates - Joonas Hämäläinen - Academy of FinlandLearn more
Available based on mandate: 11
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
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
Improving scalable K-means++
J Hämäläinen, T Kärkkäinen, T Rossi
Algorithms 14 (1), 6, 2020
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
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
Scalable robust clustering method for large and sparse data
J Hämäläinen, T Kärkkäinen, T Rossi
European Symposium on Artificial Neural Networks, Computational Intelligence …, 2018
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
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
Newton Method for Minimal Learning Machine
J Hämäläinen, T Kärkkäinen
Computational Sciences and Artificial Intelligence in Industry: New Digital …, 2022
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
A general method for structure prediction of metal-ligand interfaces of hybrid nanoparticles
S Malola, P Nieminen, J Hamalainen, T Karkkainen, H Hakkinen, ...
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