It is shown that LSR method to perform better than the rest. Then, Support Vector Machines (SVM) and Gaussian Process Regression (GPR) are tested by pairing it with LSR. In this test, the Optimizable ...

Abstract: Gaussian processes regression models are an appealing machine learning method as they learn expressive nonlinear models from exemplar data with minimal parameter tuning and estimate both the ...

We introduce a novel edge tracing algorithm using Gaussian process regression. Our edge-based segmentation algorithm models an edge of interest using Gaussian process regression and iteratively ...

STK is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at ...

This valuable study investigates how stochastic and deterministic factors are integrated during cellular decision-making, particularly in situations where cells differentiate into distinct fates ...

(1−3) However, exploring vast material spaces or conducting a large number of experiments remains challenging, making informatics-based approaches, e.g., virtual screening, genetic algorithms ... used ...

What the technology can do today—and what’s next by Thomas H. Davenport, Abhijit Guha and Dhruv Grewal Of all a company’s functions, marketing has perhaps the most to gain from artificial ...

Context. Recent studies of massive stars using high-precision space photometry have revealed that they commonly exhibit stochastic low-frequency (SLF) variability. This has been interpreted as being ...