{"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/constrained-extreme-learning-machines-a-study","title":"Constrained Extreme Learning Machines: A Study on Classification Cases","arxiv_id":"1501.06115","date":"2015-01-25","proceeding":null,"authors":["Wentao Zhu","Jun Miao","Laiyun Qing"],"abstract":"Extreme learning machine (ELM) is an extremely fast learning method and has a\npowerful performance for pattern recognition tasks proven by enormous\nresearches and engineers. However, its good generalization ability is built on\nlarge numbers of hidden neurons, which is not beneficial to real time response\nin the test process. In this paper, we proposed new ways, named \"constrained\nextreme learning machines\" (CELMs), to randomly select hidden neurons based on\nsample distribution. Compared to completely random selection of hidden nodes in\nELM, the CELMs randomly select hidden nodes from the constrained vector space\ncontaining some basic combinations of original sample vectors. The experimental\nresults show that the CELMs have better generalization ability than traditional\nELM, SVM and some other related methods. Additionally, the CELMs have a similar\nfast learning speed as ELM.","url_abs":"http://arxiv.org/abs/1501.06115v2","url_pdf":"http://arxiv.org/pdf/1501.06115v2.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":"constrained-extreme-learning-machines-a-study","repo_url":"https://github.com/wentaozhu/constrained-extreme-learning-machine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}