Table of contents
About this book
This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas. Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data.
Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, this volume features submissions from several of the working groups as well as contributions from the wider community. The volume is suitable for researchers in data science in industry and academia.
data science data analysis multiple measurement vectors statistical and topological inference hubness phenomenon multi-manifold hypothesis predictive models data storage data-driven modeling geometry-based classification Association for Women in Mathematics
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-030-11566-1
- Copyright Information The Author(s) and the Association for Women in Mathematics 2019
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics
- Print ISBN 978-3-030-11565-4
- Online ISBN 978-3-030-11566-1
- Series Print ISSN 2364-5733
- Series Online ISSN 2364-5741
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