| 书目名称 | Computational and Machine Learning Tools for Archaeological Site Modeling | | 编辑 | Maria Elena Castiello | | 视频video | | | 概述 | Nominated as an outstanding PhD thesis by the University of Bern, Switzerland.Describes novel methods for investigating archaeological settlement patterns and locational preference choices.Proposes a | | 丛书名称 | Springer Theses | | 图书封面 |  | | 描述 | This book describes a novel machine-learning based approach to answer some traditional archaeological problems, relating to archaeological site detection and site locational preferences. Institutional data collected from six Swiss regions (Zurich, Aargau, Grisons, Vaud, Geneva and Fribourg) have been analyzed with an original conceptual framework based on the Random Forest algorithm. It is shown how the algorithm can assist in the modelling process in connection with heterogeneous, incomplete archaeological datasets and related cultural heritage information. Moreover, an in-depth review of past and more recent works of quantitative methods for archaeological predictive modelling is provided. The book guides the readers to set up their own protocol for: i) dealing with uncertain data, ii) predicting archaeological site location, iii) establishing environmental features importance, iv) and suggest a model validation procedure. It addresses both academics and professionals in archaeology and cultural heritage management, and offers a source of inspiration for future research directions in the field of digital humanities and computational archaeology.. . . | | 出版日期 | Book 2022 | | 关键词 | Machine Learning in Archaeology; Random Forest in Archaeology; Computers Application in Archaeology; Co | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-88567-0 | | isbn_softcover | 978-3-030-88569-4 | | isbn_ebook | 978-3-030-88567-0Series ISSN 2190-5053 Series E-ISSN 2190-5061 | | issn_series | 2190-5053 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 19:08:03
