Joonas Hämäläinen

According to our database, Joonas Hämäläinen authored at least 15 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2023

Feature selection for distance-based regression: An umbrella review and a one-shot wrapper.

Joakim Linja, Joonas Hämäläinen, Paavo Nieminen, Tommi Kärkkäinen

Neurocomputing, 2023

Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT and GPT-4 for Cost-Efficient Question Answering.

Jonas Oppenlaender, Joonas Hämäläinen

CoRR, 2023

Minimal Learning Machine for Multi-Label Learning.

Joonas Hämäläinen, Amauri H. de Souza, César L. C. Mattos, João P. P. Gomes, Tommi Kärkkäinen

CoRR, 2023

Knowledge Discovery from Atomic Structures using Feature Importances.

Joakim Linja, Joonas Hämäläinen, Antti Pihlajamäki, Paavo Nieminen, Sami Malola, Hannu Häkkinen, Tommi Kärkkäinen

CoRR, 2023

2021

Improving Scalable K-Means++.

Joonas Hämäläinen, Tommi Kärkkäinen, Tuomo Rossi

Algorithms, 2021

Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces.

Antti Pihlajamäki, Joakim Linja, Joonas Hämäläinen, Paavo Nieminen, Sami Malola, Tommi Kärkkäinen, Hannu Häkkinen

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Instance-Based Multi-Label Classification via Multi-Target Distance Regression.

Joonas Hämäläinen, Paavo Nieminen, Tommi Kärkkäinen

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

2020

Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?

Joakim Linja, Joonas Hämäläinen, Paavo Nieminen, Tommi Kärkkäinen

Mach. Learn. Knowl. Extr., 2020

Scalable Initialization Methods for Large-Scale Clustering.

Joonas Hämäläinen, Tommi Kärkkäinen, Tuomo Rossi

CoRR, 2020

Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression.

Joonas Hämäläinen, Tommi Kärkkäinen

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019

Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection.

Joonas Hämäläinen, Alisson S. C. Alencar, Tommi Kärkkäinen, César L. C. Mattos, Amauri H. Souza Júnior, João P. P. Gomes

CoRR, 2019

2018

Scalable robust clustering method for large and sparse data.

Joonas Hämäläinen, Tommi Kärkkäinen, Tuomo Rossi

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2017

Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering.

Joonas Hämäläinen, Susanne Jauhiainen, Tommi Kärkkäinen

Algorithms, 2017

Feature Ranking of Large, Robust, and Weighted Clustering Result.

Mirka Saarela, Joonas Hämäläinen, Tommi Kärkkäinen

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2017

2016

Initialization of big data clustering using distributionally balanced folding.

Joonas Hämäläinen, Tommi Kärkkäinen

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016