首页|Findings from University of Pavol Jozef Safarik in the Area of Machine Learning Reported (A Parsimonious, Computationally Efficient Machine Learning Method for Spatial Regression)
Findings from University of Pavol Jozef Safarik in the Area of Machine Learning Reported (A Parsimonious, Computationally Efficient Machine Learning Method for Spatial Regression)
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Springer Nature
Current study results on Machine Learning have been published. According to news reporting originating in Kosice, Slovakia, by NewsRx journalists, research stated, "We introduce the modified planar rotator method (MPRS), a physically inspired machine learning method for spatial/temporal regression. MPRS is a non-parametric model which incorporates spatial or temporal correlations via shortrange, distance-dependent 'interactions' without assuming a specific form for the underlying probability distribution." Financial support for this research came from Vedeck Grantov Agentra MScaron;VVaScaron; SR a SAV. The news reporters obtained a quote from the research from the University of Pavol Jozef Safarik, "Predictions are obtained by means of a fully autonomous learning algorithm which employs equilibrium conditional Monte Carlo simulations. MPRS is able to handle scattered data and arbitrary spatial dimensions. We report tests on various synthetic and real-word data in one, two and three dimensions which demonstrate that the MPRS prediction performance (without hyperparameter tuning) is competitive with standard interpolation methods such as ordinary kriging and inverse distance weighting. MPRS is a particularly effective gap-filling method for rough and non-Gaussian data (e.g., daily precipitation time series). MPRS shows superior computational efficiency and scalability for large samples. Massive datasets involving millions of nodes can be processed in a few seconds on a standard personal computer."
KosiceSlovakiaEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Pavol Jozef Safarik
NETL
NSTL
Springer Nature
