Mathematical optimization in classification and regression trees
Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …
Machine Learning. In this paper, we review recent contributions within the Continuous …
Nearest‐neighbor sparse Cholesky matrices in spatial statistics
A Datta - Wiley Interdisciplinary Reviews: Computational …, 2022 - Wiley Online Library
Gaussian process (GP) is a staple in the toolkit of a spatial statistician. Well‐documented
computing roadblocks in the analysis of large geospatial datasets using GPs have now …
computing roadblocks in the analysis of large geospatial datasets using GPs have now …
Point break: using machine learning to uncover a critical mass in women's representation
Decades of research has debated whether women first need to reach a “critical mass” in the
legislature before they can effectively influence legislative outcomes. This study contributes …
legislature before they can effectively influence legislative outcomes. This study contributes …
Spatial prediction of apartment rent using regression-based and machine learning-based approaches with a large dataset
Employing a large dataset (at most, the order of n= 106), this study attempts enhance the
literature on the comparison between regression and machine learning-based rent price …
literature on the comparison between regression and machine learning-based rent price …
Pilot Tones Design for Channel Estimation Using Elephant Herding Optimization Algorithm in Massive MIMO Systems
N Taşpınar, A Ergeç, BK Gül - Wireless Personal Communications, 2023 - Springer
Today, the increase in the demand for mobile communication and the increasing need for
data transfer have reached great dimensions. In order to meet this need, multi-input multi …
data transfer have reached great dimensions. In order to meet this need, multi-input multi …
Unbiased Estimation of Structured Prediction Error
K Fry, JE Taylor - arXiv preprint arXiv:2310.10740, 2023 - arxiv.org
Many modern datasets, such as those in ecology and geology, are composed of samples
with spatial structure and dependence. With such data violating the usual independent and …
with spatial structure and dependence. With such data violating the usual independent and …
Sparse nearest neighbor Cholesky matrices in spatial statistics
A Datta - arXiv preprint arXiv:2102.13299, 2021 - arxiv.org
Gaussian Processes (GP) is a staple in the toolkit of a spatial statistician. Well-documented
computing roadblocks in the analysis of large geospatial datasets using Gaussian …
computing roadblocks in the analysis of large geospatial datasets using Gaussian …