{"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/semi-supervised-regression-with-co-training","title":"Semi-Supervised Regression with Co-Training","arxiv_id":null,"date":"2005-06-01","proceeding":null,"authors":["Zhi-Hua Zhou","Ming Li"],"abstract":"In many practical machine learning and data min-ing  applications,  unlabeled  training  examples  arereadily available but labeled ones are fairly expen-sive  to  obtain.   Therefore,  semi-supervised  learn-ing  algorithms  such  asco-traininghave  attractedmuch attention. Previous research mainly focuseson semi-supervised classification.  In this paper, aco-training style semi-supervised regression algo-rithm,  i.e.COREG,  is  proposed.   This  algorithmuses twok-nearest neighbor regressors with differ-ent distance metrics, each of which labels the unla-beled data for the other regressor where the label-ing confidence is estimated through consulting theinfluence of the labeling of unlabeled examples onthe labeled ones.   Experiments show thatCOREGcan  effectively  exploit  unlabeled  data  to  improveregression estimates.","url_abs":"https://www.mit.bme.hu/eng/system/files/oktatas/targyak/7153/SemiSupervisedRegressionWithCoTraining_Zhou.pdf","url_pdf":"https://www.mit.bme.hu/eng/system/files/oktatas/targyak/7153/SemiSupervisedRegressionWithCoTraining_Zhou.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":"semi-supervised-regression-with-co-training","repo_url":"https://github.com/nealjean/coreg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"semi-supervised-regression-with-co-training","repo_url":"https://github.com/nikola310/indoor-localization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"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}