{"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/active-learning-for-regression-using-greedy","title":"Active Learning for Regression Using Greedy Sampling","arxiv_id":"1808.04245","date":"2018-08-08","proceeding":null,"authors":["Dongrui Wu","Chin-Teng Lin","Jian Huang"],"abstract":"Regression problems are pervasive in real-world applications. Generally a\nsubstantial amount of labeled samples are needed to build a regression model\nwith good generalization ability. However, many times it is relatively easy to\ncollect a large number of unlabeled samples, but time-consuming or expensive to\nlabel them. Active learning for regression (ALR) is a methodology to reduce the\nnumber of labeled samples, by selecting the most beneficial ones to label,\ninstead of random selection. This paper proposes two new ALR approaches based\non greedy sampling (GS). The first approach (GSy) selects new samples to\nincrease the diversity in the output space, and the second (iGS) selects new\nsamples to increase the diversity in both input and output spaces. Extensive\nexperiments on 12 UCI and CMU StatLib datasets from various domains, and on 15\nsubjects on EEG-based driver drowsiness estimation, verified their\neffectiveness and robustness.","url_abs":"http://arxiv.org/abs/1808.04245v1","url_pdf":"http://arxiv.org/pdf/1808.04245v1.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":"active-learning-for-regression-using-greedy","repo_url":"https://github.com/drwuHUST/iGS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.04245","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}