Au38Q MBTR-K3
Linja, Joakim; Hämäläinen, Joonas; Kärkkäinen, Tommi; Nieminen, Paavo. (2020). Au38Q MBTR-K3. V. 11.11.2020. Zenodo. https://doi.org/10.5281/zenodo.4268064.
JYU authors:
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All authors: Linja, Joakim; Hämäläinen, Joonas; Kärkkäinen, Tommi; Nieminen, Paavo
Funders: Research Council of Finland
Right-holders:
Availability and identifiers
Availability: Direct download
Publication year: 2020
Persistent identifiers of the dataset: doi:10.5281/zenodo.4268064
DOI identifier in original repository: https://doi.org/10.5281/zenodo.4268064
Description of the dataset
We used three different numbers of observations and three different numbers of descriptor accuracies. Regarding the the number of observations, we used RS-maximin to find out the most different observations available and used the first 4000 and first 8000 as the selections in 4k and 8k variants. Regarding the number of features, we used different descriptor accuracy values [2,10,100] that produced descriptors of lengths [80,400,4000]. This allowed the number of features to represent the data description resolution. Downsampling of the number of features from 4000 to lower numbers was not used.
Further details are presented in paper Do Randomized Algorithms Improve the Efficiency of
Minimal Learning Machine? by Linja et al.
Language: English
Free keywords: Machine learning; Regression; Many Body Tensor Representation; MBTR; Hybrid nanoparticles
Keywords (YSO): machine learning; regression analysis
Fields of science: 113 Computer and information sciences
Follow-up groups: Learning and Cognitive Sciences (Faculty of Information Technology IT) LEACS; Human and Machine based Intelligence in Learning (Faculty of Information Technology IT) HUMBLE; Degree Education (Faculty of Information Technology IT) TUTK; Computing Education Research (Faculty of Information Technology IT) CER; Engineering (Faculty of Information Technology IT) OHTE; Formerly Software and Communications Engineering
Do you deal with data concerning special categories of personal data in your research?: No
Projects related to dataset
- STRUCTURE PREDICTION OF HYBRID NANOPARTICLES VIA ARTIFICIAL INTELLIGENCE
- Kärkkäinen, Tommi
- Research Council of Finland