Artificial Intelligence &amp; Machine Learning in the Earth Sciences

Acta Geologica Sinica - English EditionVolume 93, Issue S3 p. 48-51 PART 1: Abstracts of Deep-time Digital Earth (DDE) Forum February 26 – 28, 2019, Beijing, ChinaFree Access Artificial Intelligence & Machine Learning in the Earth Sciences Norman MacLEOD, Corresponding Author Norman MacLEOD N.MacLeod9@gmail.com The Natural History Museum, Cromwell Road, London, SW7 5BD UK Current address: School of Earth Sciences and Engineering, Nanjing University, 163# Xianlin Avenue, Nanjing 210023 ChinaCorresponding author. E-mail: N.MacLeod9@gmail.comSearch for more papers by this author Norman MacLEOD, Corresponding Author Norman MacLEOD N.MacLeod9@gmail.com The Natural History Museum, Cromwell Road, London, SW7 5BD UK Current address: School of Earth Sciences and Engineering, Nanjing University, 163# Xianlin Avenue, Nanjing 210023 ChinaCorresponding author. E-mail: N.MacLeod9@gmail.comSearch for more papers by this author First published: 31 May 2019 https://doi.org/10.1111/1755-6724.14241Citations: 5 About the author: Norman MacLEOD, male, born in 1953 in Augsburg, Germany; PhD; Researcher, The Natural History Museum, London, UK. He is interested in the theory, methods, and applications involved in the quantitative analysis of morphological variation across the life and earth sciences. Email: N.MacLeod9@gmail.com; phone (+44) (0)785 1017-1787. AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat References Caté, A., et al., 2017. The Leading Edge, 2017(3): 64– 68. Culverhouse, P.F., et al., 2014. Marine Biology Research, 10: 73– 84. Culverhouse, P.F., et al., 2003. Marine Ecology Progress Series, 247: 17– 25. Hardy, G.H., 1940. A mathematician's apology. Cambridge University Press, Cambridge, UK. Isbell, L. A., 2011. The fruit, the tree, and the serpent: why we see so well. Harvard University Press, Cambridge, Massachusetts. Kirkwood, C., et al., 2016. Journal of Geochemical Exploration, 167: 49– 61. Lary, D.J., et al., 2016. Geoscience Frontiers, 7: 3– 10. LeCun, Y., et al., 1998. Proceedings of the IEEE, 86: 2278– 2324. MacLeod, N., 2007. Automated taxon identification in systematics: theory, approaches, and applications, CRC Press, London. MacLeod, N., 2017. On the use of machine learning methods in morphometric analysis. In Lestrel, P.E. ed., Proceedings of the Fourth International Symposium on Biological Shape Analysis, World Scientific, Singapore, p. 134– 171. Minsky, M., and Papert, S.A., 1969. Perceptrons: an introduction to computational geometry, MIT Press, Cambridge, Massachusetts. Rodriguez-Galiano, et al., 2015. Ore Geology Reviews, 71: 804– 818. Rosenblatt, F., 1958. Psychological Review, 65: 386– 408. Rumelhart, D.E., et al., 1986. Learning internal representations by error propagation, In: D.E. Rumelhart et al., Parallel distributed processing: explorations in the microstructure of cognition, Volume 1, Foundation, MIT Press, Cambridge, Massachusetts, p. 318– 362. Samuel, A.L., 1959. IBM Journal of Research and Development, 3: 210– 229. Sarkar, D., 2018. Medium 2018(11): 1– 83. Tarca, A.L., et al., 2007. PLoS Computational Biology, 3: 953– 963. van der Maaten, L., et al., 2006. Computer vision and machine learning for archaeology, In Clark, J.T. and Hagemeister, E.M. eds., Proceedings of the 34th Conference, Fargo, North Dakota, Archaeolingua, Budapest p. 476– 482. Walsh, T., 2017. Medium, 2017(10): 1– 12. Werbos, P.J., 1974. Beyond regression: new tools for prediction and analysis in the behavioral sciences: Harvard University. Citing Literature Volume93, IssueS3Special Issue: Abstracts of Deep-time Digital Earth (DDE) Forum, 26–28 Feb 2019, Beijing, ChinaMay 2019Pages 48-51 ReferencesRelatedInformation