{"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/a-variance-maximization-criterion-for-active","title":"A Variance Maximization Criterion for Active Learning","arxiv_id":"1706.07642","date":"2017-06-23","proceeding":null,"authors":["Yazhou Yang","Marco Loog"],"abstract":"Active learning aims to train a classifier as fast as possible with as few\nlabels as possible. The core element in virtually any active learning strategy\nis the criterion that measures the usefulness of the unlabeled data based on\nwhich new points to be labeled are picked. We propose a novel approach which we\nrefer to as maximizing variance for active learning or MVAL for short. MVAL\nmeasures the value of unlabeled instances by evaluating the rate of change of\noutput variables caused by changes in the next sample to be queried and its\npotential labelling. In a sense, this criterion measures how unstable the\nclassifier's output is for the unlabeled data points under perturbations of the\ntraining data. MVAL maintains, what we refer to as, retraining information\nmatrices to keep track of these output scores and exploits two kinds of\nvariance to measure the informativeness and representativeness, respectively.\nBy fusing these variances, MVAL is able to select the instances which are both\ninformative and representative. We employ our technique both in combination\nwith logistic regression and support vector machines and demonstrate that MVAL\nachieves state-of-the-art performance in experiments on a large number of\nstandard benchmark datasets.","url_abs":"http://arxiv.org/abs/1706.07642v2","url_pdf":"http://arxiv.org/pdf/1706.07642v2.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":"a-variance-maximization-criterion-for-active","repo_url":"https://github.com/YazhouTUD/MVAL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"informativeness","task_name":"Informativeness"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}