{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:51:15Z","timestamp":1648860675396},"reference-count":6,"publisher":"Cambridge University Press (CUP)","issue":"S325","license":[{"start":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T00:00:00Z","timestamp":1496102400000},"content-version":"unspecified","delay-in-days":241,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. IAU"],"published-print":{"date-parts":[[2016,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Many astronomers working in the field of AstroInformatics write code as part of their work. Although the programming language of choice is Python, a small number (8%) use R. R has its specific strengths in the domain of statistics, and is often viewed as limited in the size of data it can handle. However, Microsoft R Server is a product that removes these limitations by being able to process much larger amounts of data. I present some highlights of R Server, by illustrating how to fit a convolutional neural network using R. The specific task is to classify galaxies, using only images extracted from the Sloan Digital Skyserver.<\/jats:p>","DOI":"10.1017\/s1743921317003520","type":"journal-article","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T11:04:51Z","timestamp":1496142291000},"page":"139-144","source":"Crossref","is-referenced-by-count":0,"title":["Classification of galaxy type from images using Microsoft R Server"],"prefix":"10.1017","volume":"12","author":[{"given":"Andrie","family":"de Vries","sequence":"first","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2017,5,30]]},"reference":[{"key":"S1743921317003520_ref002","unstructured":"Nasa, Hubble reveals observable universe contains 10 times more galaxies than previously thought. https:\/\/www.nasa.gov\/feature\/goddard\/2016\/hubble-reveals-observable-universe-contains-10-times-more-galaxies-than-previously-thought Accessed: 2016-11-29."},{"key":"S1743921317003520_ref006","unstructured":"R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. ISBN 3-900051-07-0."},{"key":"S1743921317003520_ref003","unstructured":"Microsoft, Microsoft r server website https:\/\/msdn.microsoft.com\/en-us\/microsoft-r\/. Accessed: 2016-11-29."},{"key":"S1743921317003520_ref004","unstructured":"Microsoft, R server getting started. https:\/\/msdn.microsoft.com\/en-us\/microsoft-r\/microsoft-r-getting-started. Accessed: 2016-11-29."},{"key":"S1743921317003520_ref001","unstructured":"Galaxyzoo web site, https:\/\/www.galaxyzoo.org\/. Accessed: 2016-11-29."},{"key":"S1743921317003520_ref005","doi-asserted-by":"crossref","unstructured":"Honglak Lee , Roger Grosse , Rajesh Ranganath , and Andrew Y. Ng , Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML \u201909, pages 609\u2013616, New York, NY, USA, 2009. ACM.","DOI":"10.1145\/1553374.1553453"}],"container-title":["Proceedings of the International Astronomical Union"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S1743921317003520","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T23:27:26Z","timestamp":1555630046000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S1743921317003520\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,10]]},"references-count":6,"journal-issue":{"issue":"S325","published-print":{"date-parts":[[2016,10]]}},"alternative-id":["S1743921317003520"],"URL":"https:\/\/doi.org\/10.1017\/s1743921317003520","relation":{},"ISSN":["1743-9213","1743-9221"],"issn-type":[{"value":"1743-9213","type":"print"},{"value":"1743-9221","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,10]]}}}