{"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/interactive-learning-from-multiple-noisy","title":"Interactive Learning from Multiple Noisy Labels","arxiv_id":"1607.06988","date":"2016-07-24","proceeding":null,"authors":["Shankar Vembu","Sandra Zilles"],"abstract":"Interactive learning is a process in which a machine learning algorithm is\nprovided with meaningful, well-chosen examples as opposed to randomly chosen\nexamples typical in standard supervised learning. In this paper, we propose a\nnew method for interactive learning from multiple noisy labels where we exploit\nthe disagreement among annotators to quantify the easiness (or meaningfulness)\nof an example. We demonstrate the usefulness of this method in estimating the\nparameters of a latent variable classification model, and conduct experimental\nanalyses on a range of synthetic and benchmark datasets. Furthermore, we\ntheoretically analyze the performance of perceptron in this interactive\nlearning framework.","url_abs":"http://arxiv.org/abs/1607.06988v1","url_pdf":"http://arxiv.org/pdf/1607.06988v1.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":"interactive-learning-from-multiple-noisy","repo_url":"https://github.com/svembu/ilearn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}