Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selection.
Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives.
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
A cosmic ray consists of mostly highly energetic protons that emanate from the sun, the Milky Way and distant galaxies.
Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations.
It is substantially faster than the interior point method, and just as accurate.
Computation Methodology
For this purpose, we develop a novel context-free grammar that enables the automated generation of multigrid methods in a symbolically-manipulable formal language, based on which we can apply the same multigrid-based solver to problems of different sizes without having to adapt its internal structure.
In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties.
Materials Science Soft Condensed Matter Computational Physics
We present fast, general methods for fitting sparse matrix linear models to structured high-throughput data.
Computation
In this paper, we present a machine learning method for the discovery of analytic solutions to differential equations.