{"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/fuzzy-bayesian-learning","title":"Fuzzy Bayesian Learning","arxiv_id":"1610.09156","date":"2016-10-28","proceeding":null,"authors":["Indranil Pan","Dirk Bester"],"abstract":"In this paper we propose a novel approach for learning from data using rule\nbased fuzzy inference systems where the model parameters are estimated using\nBayesian inference and Markov Chain Monte Carlo (MCMC) techniques. We show the\napplicability of the method for regression and classification tasks using\nsynthetic data-sets and also a real world example in the financial services\nindustry. Then we demonstrate how the method can be extended for knowledge\nextraction to select the individual rules in a Bayesian way which best explains\nthe given data. Finally we discuss the advantages and pitfalls of using this\nmethod over state-of-the-art techniques and highlight the specific class of\nproblems where this would be useful.","url_abs":"http://arxiv.org/abs/1610.09156v2","url_pdf":"http://arxiv.org/pdf/1610.09156v2.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":"fuzzy-bayesian-learning","repo_url":"https://github.com/SciemusGithub/FBL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}