{"product_id":"computational-and-machine-learning-tools-for-archaeological-site-modeling-9783030885694","title":"Computational and Machine Learning Tools for Archaeological Site Modeling","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThis book uses machine learning to address archaeological problems such as site detection and locational preferences, analyzing institutional data from six Swiss regions. It demonstrates how the Random Forest algorithm can assist in modeling processes with heterogeneous and incomplete datasets and provides an in-depth review of quantitative methods for archaeological predictive modeling. It is a valuable resource for academics and professionals in archaeology and cultural heritage management. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 296 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 26 January 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Nature Switzerland AG\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThis captivating book delves into a groundbreaking machine-learning-based approach to address a range of traditional archaeological challenges, including archaeological site detection and site locational preferences. By utilizing institutional data collected from six Swiss regions (Zurich, Aargau, Grisons, Vaud, Geneva, and Fribourg), the author has developed an innovative conceptual framework rooted in the powerful Random Forest algorithm. Through meticulous analysis, the book showcases how this algorithm can effectively aid in modeling processes, particularly when dealing with diverse and incomplete archaeological datasets and associated cultural heritage information. Moreover, an extensive review of past and recent quantitative methods for archaeological predictive modeling is presented, providing valuable insights for readers.\u003cbr\u003e\u003cbr\u003eThe book serves as a comprehensive guide, equipping readers with the necessary tools to establish their protocol for handling uncertain data, predicting archaeological site locations, assessing the importance of environmental features, and proposing a robust model validation procedure. Its interdisciplinary appeal extends to academics and professionals in archaeology and cultural heritage management, offering a rich source of inspiration for future research endeavors in the realm of digital humanities and computational archaeology.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 486g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9783030885694\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2022\u003c\/p\u003e","brand":"Maria Elena Castiello","offers":[{"title":"Paperback \/ softback","offer_id":44302335803642,"sku":"9783030885694","price":183.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_2eff18d1-a1cc-4f3c-83dd-6f7fef3620c5.jpg?v=1687925303","url":"https:\/\/shulphink.com\/products\/computational-and-machine-learning-tools-for-archaeological-site-modeling-9783030885694","provider":"Shulph Ink","version":"1.0","type":"link"}