{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-bayesian-perspective-of-statistical-machine","title":"A Bayesian Perspective of Statistical Machine Learning for Big Data","arxiv_id":"1811.04788","date":"2018-11-09","proceeding":null,"authors":["Rajiv Sambasivan","Sourish Das","Sujit K Sahu"],"abstract":"Statistical Machine Learning (SML) refers to a body of algorithms and methods\nby which computers are allowed to discover important features of input data\nsets which are often very large in size. The very task of feature discovery\nfrom data is essentially the meaning of the keyword `learning' in SML.\nTheoretical justifications for the effectiveness of the SML algorithms are\nunderpinned by sound principles from different disciplines, such as Computer\nScience and Statistics. The theoretical underpinnings particularly justified by\nstatistical inference methods are together termed as statistical learning\ntheory.\n  This paper provides a review of SML from a Bayesian decision theoretic point\nof view -- where we argue that many SML techniques are closely connected to\nmaking inference by using the so called Bayesian paradigm. We discuss many\nimportant SML techniques such as supervised and unsupervised learning, deep\nlearning, online learning and Gaussian processes especially in the context of\nvery large data sets where these are often employed. We present a dictionary\nwhich maps the key concepts of SML from Computer Science and Statistics. We\nillustrate the SML techniques with three moderately large data sets where we\nalso discuss many practical implementation issues. Thus the review is\nespecially targeted at statisticians and computer scientists who are aspiring\nto understand and apply SML for moderately large to big data sets.","url_abs":"http://arxiv.org/abs/1811.04788v2","url_pdf":"http://arxiv.org/pdf/1811.04788v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-bayesian-perspective-of-statistical-machine","repo_url":"https://github.com/fraziezr/Machine_Learning_Final_Project_Team_12","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"learning-theory","task_name":"Learning Theory"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}